# Using Deep Learning to Improve Early Diagnosis of Pneumonia in
Underdeveloped Countries

Kyler Larsen

(November 2021)

##  1 Introduction

Increasing efficiency and accuracy for the diagnosis of pneumonia can decrease
thousands of preventable deaths each year. Pneumonia is a common disease in
the community that can be easily treated with inexpensive oral antibiotics
administered by volunteer health care workers (Pneumonia 2021). However, if
there is any delay in diagnosis or treatment, the disease course can be very
serious and even deadly. Many of these diagnoses can be made through
identification of symptoms such as cough, fever, or an increased respiratory
rate. Chest x-ray is an inexpensive but vital component to diagnose pneumonia
and to rule out other diseases. Certain types of pneumonia, especially
atypical pneumonia such as Mycoplasma pneumoniae, can infect a person for
weeks with no discernable symptoms. These types of pneumonia account for 10 to
40 percent of the cases of community acquired pneumonia in the United States
(Atypical 2021). With this type of pneumonia it is even more important to use
chest x-rays as an effective tool for accurate diagnosis.

In underdeveloped countries, specifically in Sub-Saharan Africa and Southeast
Asia, the death rate from pneumonia was almost 8-10 times the death rate in
the United States in 2017 (Dadonaite et al 2021). Although many countries in
Sub-Saharan Africa, such as Cameroon, receive medical equipment, including
x-ray machines, through donation services, up to 70% of the equipment is never
used (Piaggio et al 2009). One primary cause of this problem is the lack of
doctors and trained healthcare workers. In the Philippines, it was estimated
that 85% of Filipino nurses were working overseas in Western countries,
leading to 70% of Filipinos dying without medical attention (Finch 2013). In
Sub-Saharan Africa, a critical shortage of doctors and nurses amounting to 2.4
million personnel in the years 2000-2006, hampered the ability of many health
programs in place to detect and prevent chronic diseases (Naicker et al 2010).
These statistics show that a shortage of trained medical personnel in
underdeveloped countries causes delayed diagnoses or lack of proper treatment,
leading to possible preventable deaths. This study will focus on evaluating a
deep learning model, specifically over how well it can detect pneumonia in
chest x-rays, and adopting the model to become an affordable and accurate
alternative that will save many lives.

Previous studies have been conducted over the effectiveness of different AI
models in limiting pneumonia’s harmful effects. For example, various machine
learning models were evaluated on their accuracy in predicting severe
pneumonia, which would allow proper treatment before the harmful effects could
take place. The models evaluated in the study were the Support Vector Machine,
Logistic Regression model, Random Forest Classifier, Naive Bayes, and
AdaBoost. Ultimately, the study concluded that the Random Forest Classifier
performed the best, due to its superior stability (Luo et al 2020). Another
study evaluated a deep learning transfer model, VGG-16, as to how well it
could identify pneumonia caused by COVID-19. The study also used chest x-ray
analysis to form the model’s diagnoses, but concluded that further
improvements could be made by adjusting hyperparameters and transfer learning
combinations (M. D. Hasan et al 2021). This study attempts to fill this gap by
evaluating a basic Convolutional Neural Network (CNN) by experimenting with
hyperparameters to achieve maximum metrics.

The purpose of this study was to evaluate the performance of a CNN model given
input in the form of chest x-rays. This study hypothesizes that the changing
hyperparameters of the model can produce results equivalent to a trained
physician. The null hypothesis assumes the model would not improve through
training or would not achieve results similar to a physician. By testing
various hyperparameters and extracting results based on key metrics,
conclusions can be drawn as to how well the model performed (M. D. Hasan et al
2021). The significance of this study is to provide information regarding the
feasibility of using a basic CNN model to diagnose accurately at point of care
when there is shortage of trained healthcare workers. By developing and
evaluating such a model, this study could save lives by improving efficiency
and accuracy, as well as saving money and time.

##  2 Methods

In this study, the first step was to organize the data set. The data set used
in this study is from Kaggle (Patel 2021), and features 6432 pre-classified
images. This dataset includes 3 separate categories: COVID-19 pneumonia,
general pneumonia, and normal lung x-rays. For this study, only images from
the general pneumonia and normal lung x-rays were used, amounting to 2400
images with 1200 images from each classification. The data was then reshaped
and augmented to provide a variety of data and to eliminate the need for
“perfect” data. Augmentation included stretching certain pixels or rotating
the image, allowing the creation of extra data as each image could have
multiple augmentations done to it. The data was then split into 80% for
training data and 20% for testing data. The training data is the data used by
the model to learn its methods, while the testing data is used to evaluate the
model’s accuracy.

A CNN works by receiving input in the form of an image. Then, through a
hierarchical system, the various layers of the CNN create a network that
resembles connected neurons, similar to the human brain. At each layer, an
activation function transforms the results of each layer into specific output.
The activation function used in this study is the Rectified Linear Activation
Unit (ReLU), which returns the sum of each node’s input, or 0 if the sum is
less than 0 (Brownlee 2021). The CNN then applies weights and flattens the
output before it is fed to a feed-forward neural network and backpropagation
is applied to every iteration of training. Over a series of these iterations,
the model is able to distinguish between important dominant features and less-
valuable low-level features (Brownlee 2021).

CNNs are valuable for identifying and classifying images, making it the ideal
model for this study. In this study, the CNN was defined as sequential, since
it only takes in one input and produces only one output, allowing for the
addition of multiple layers. An initial layer of 32 filters with ReLU
activation was then added, with a max pooling size of (2,2) to reduce output
size. Then, additional layers with 64 filters were added with ReLU activation
before the output was flattened. The amount of added layers is a tested
variable, meaning it needs to be non-specific. Dropout requirements were also
set at 0.5 in order to reduce overfitting, which is when the model memorizes
the data set instead of forming its own predictions. In this case, 50% of the
neurons will be ignored during each iteration, forcing the model to learn new
patterns each time (Brownlee 2021). Finally, the output is formatted and set
to print loss function values, accuracy metrics, specificity, and sensitivity.

For the testing phase of the model, experimentation was done through changing
the model’s hyperparameters. The first parameter experimented with was the
number of extra layers added to the model, allowing more determinations to be
made at the expense of overfitting. Other parameters include the number of
epochs, or iterations, made by the model through the data set during each
training cycle, and the set sensitivity of the model, allowing for the maximum
sensitivity while still holding the other metrics constant. For each change to
the parameters, 5 models were trained and the average values for each
specificity, accuracy, precision, recall, and f1 score were taken to ensure
accurate results. The output displays the accuracy and specificity of the
model after each model is trained, as well as a graph depicting the trend of
the accuracy over each epoch.

##  3 Results

The primary focus of the trials in this study is to maximize sensitivity while
keeping specificity as high as possible. Sensitivity is defined by

meaning it determines how well the model identifies the disease. Specificity
is defined by

which focuses on correctly identifying patients without the disease. The first
trial increased sensitivity while monitoring specificity and accuracy, with
the number of epochs set to 5 and the number of layers set to 3. From the
graph shown in Figure 1 below, it was determined that the maximum sensitivity
was around 85%.

The amount of extra layers was then adjusted, ultimately showing that the
optimum sensitivity occurred with just 2 extra layers, resulting in a 90%
sensitivity with a 77.7% specificity and an 82.5% accuracy. To verify that
these were the optimum values the model could produce, the amount of epochs
was adjusted.

From Figures 2 and 3, it was determined that 5 epochs was the optimum number
of epochs, in terms of largest specificity and accuracy. This conclusion was
further verified by its correlation to the f1-score of the test shown in
Figure 4, which relies on both precision and recall.

##  4 Discussion

The main focus of this study was to evaluate the performance of a CNN model
over an x-ray dataset, while maximizing sensitivity. The main reason for
optimizing sensitivity was the nature of pneumonia and its treatments.
Maximizing sensitivity focuses on eliminating false negatives at the cost of
false positives, but ensures that almost all cases of pneumonia are
identified. Maximizing specificity would focus on eliminating false positives
at the expense of false negatives, meaning more cases of pneumonia would go
undiagnosed. For studies similar to this one, the maximized metric will
typically be positive predictive value, due to its measure of true positive
accuracy. However, the goal of this model is to catch as many cases of
pneumonia as possible, and maximizing the positive predictive value weighs
both sensitivity and specificity equally. The treatment, especially if an
early diagnosis is made, generally has minimal detrimental effects, meaning a
false negative would have a more serious effect than a false positive. The
primary treatment for bacterial pneumonia is antibiotics, which would likely
have minimal negative effects on the person if taken with a false positive
result (Pneumonia 2021). This means that while the rate of false negatives is
important, the primary issue should be diagnosing all pneumonia positive
images as positive rather than determining the accuracy of all positive
diagnoses. A primary issue faced in this study was the issue of overfitting.
Overfitting happens when the model is trained too many times on the same data
set, causing it to start memorizing the data set rather than learning from its
attributes (Muralidhar 2021). Overfitting can be identified graphically and
statistically.

Figure 5.1 shows when the model in the study has successfully learned a data
set without overfitting, while Figure 5.2 shows when the model started
overfitting. The first main differences are that the training line begins to
plateau as it increases, which Figure 5.2 does to a greater degree than the
left. Additionally, the training data’s accuracy surpasses the validation’s
(test data) accuracy much sooner on Figure 5.2, illustrating a faster rate of
learning which is common among overfitting models. This can also be identified
numerically.

Figure 6.1 shows results from the 5 epochs of the optimal CNN model, and
Figure 6.2 shows the last 5 epochs from the CNN with training 20 epochs.
Figure 6.1 shows a stagnant accuracy measurement, indicating the model is no
longer learning, but rather memorizing, while Figure 6.2’s accuracy increases
drastically. Additionally, the training accuracy for Figure 6.2 is much higher
than the testing accuracy, which is also decreasing, indicating the model is
not learning anymore, causing it to perform worse on the test sets. This
demonstrates why a lower amount of epochs and training layers was more
successful, as they prevented overfitting by reducing the amount of training.
The danger with this is that it can cause underfitting, which happens when the
model has not learned enough, causing bias towards the attributes it has
learned. It is, therefore, important to test the model on as many values as
possible. Overfitting was prevented by adding dropout layers and by lowering
the amount of epochs and hidden layers of the model.

##  5 Conclusion

This study illustrates that the proposed model will be able to help those who
cannot have proper access to physicians for pneumonia diagnosis. The model
achieved a sensitivity of 90%, a specificity of 77%, an accuracy of 82.5%, and
an f1 score of 0.866. These results are only passable, and overall provide
more evidence to support the null hypothesis, because certainty is imperative
when it comes to human lives. However, the results do indicate that the model
has potential to save lives. The model was able to succeed in reducing
overfitting, as well as preventing underfitting. A model like this one could
be deployed as either an online or offline program able to receive x-rays in
the form of images and return a positive or negative pneumonia diagnosis. No
medical expertise other than an x-ray technician would be needed, allowing
underserved areas to receive timely medical care, despite the shortage of
medical personnel. Further research can explore the possibility of transfer
learning as an alternative to the basic CNN, which could increase diagnosis
accuracy. Another important advancement could focus on applying this
technology and methodology to other lung-based diseases that can be diagnosed
through chest x-rays, such as tuberculosis. Therefore, with further research,
the model can be improved so that underdeveloped countries without access to
quality doctors or nurses can still receive the healthcare they need in a
timely and affordable fashion.

##  6 Literature Cited

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the-eli5-way-3bd2b1164a53.


# A Mamba-based Siamese Network for Remote Sensing Change Detection

Jay N. Paranjape
Johns Hopkins University, Baltimore
<EMAIL_ADDRESS>
Celso de Melo
DEVCOM Army Research Laboratory, Adelphi
<EMAIL_ADDRESS>
Vishal M. Patel
Johns Hopkins University, Baltimore
<EMAIL_ADDRESS>

###### Abstract

Change detection in remote sensing images is an essential tool for analyzing a
region at different times. It finds varied applications in monitoring
environmental changes, man-made changes as well as corresponding decision-
making and prediction of future trends. Deep learning methods like
Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable
success in detecting significant changes, given two images at different times.
In this paper, we propose a Mamba-based Change Detector (M-CD) that segments
out the regions of interest even better. Mamba-based architectures demonstrate
linear-time training capabilities and an improved receptive field over
transformers. Our experiments on four widely used change detection datasets
demonstrate significant improvements over existing state-of-the-art (SOTA)
methods. Our code and pre-trained models are available at
https://github.com/JayParanjape/M-CD

##  1 Introduction

In remote sensing, Change Detection (CD) refers to the task of detecting
significant changes to our planet’s surface across time. It is generally
addressed by processing satellite images of a region, taken at different
instances in time [38, 2]. The task of CD is non-trivial since the change of
interest can vary across different applications, ranging from man-made
structures, natural vegetation, or effects of climate change. Subsequently, CD
finds its applications in monitoring and assessing natural disasters and
climate shifts [43, 50, 26, 28, 69], policy planning [55, 7], surveying land
and farmland cover [37, 16, 61], as well as military applications [62, 36].
Remote sensing images over different points in time can have multiple data
disparities, including illumination changes [63, 42], varying resolutions [5],
noise [1, 39], and errors in registration [31, 6] among others. Thus, making
CD systems that are robust to such discrepancies is a complex and challenging
task.

With the advent of deep learning, various systems have been developed for CD
that employ deep networks and show substantial gains on multiple datasets [21,
44, 11, 4, 3, 71]. These methods include Convolutional Neural Networks [21, 8,
44], Attention-based networks [4, 11, 12] as well as diffusion-based networks
[3]. Deep learning-based methods for CD generally take in a pair, consisting
of a pre-change image and a post-change image as input, and output a
segmentation mask locating the regions where the change of interest has
occurred.

Figure 1: (Top) Example result of our M-CD. (Bottom) Average performance of
M-CD with respect to existing types of CD models

Recently, selective state space models (a.k.a Mamba) [22] were proposed that
have a more global receptive field than transformers and are more scalable.
While these were introduced for text inputs, VMamba [45] adapted this model
for representing images and showed improvements in general vision tasks like
classification [45] and segmentation [64]. In this paper, we propose a Mamba-
based Change Detector (M-CD) that outperforms all existing SOTA CD methods, as
shown in Fig. 1. In particular, we develop a VMamba-based Siamese image
encoder that is used to generate rich features from the two input images. We
then develop a concatenation-based difference module that takes the features
for the two images as input and combines them at multiple scales to create a
joint feature vector. This is passed to a VMamba-based decoder that performs
an aggregation over channels to produce the final output mask. In summary, the
main contributions of our work are as follows:

  1. 1.

We develop a novel Mamba-based Change Detector, called M-CD, that generates
precise change masks, given two images.

  2. 2.

To this extent, we develop a difference module that combines the features from
the pair of images. This is provided to the mask decoder, which is modified to
be more aware of the channel information along with the temporal and spatial
information.

  3. 3.

We evaluate our method on four widely used public datasets. We demonstrate
consistent improvements over existing SOTA methods.

##  2 Related Work

Classical Change Detection. Traditionally, CD has been approached using
algebraic, transformation-based or classification-based methods. Algebraic
methods generally perform predefined mathematical operations to find a
difference measure between the pre-change and post-change images. This is
followed by using a pre-defined threshold to detect changed areas in the
image. Some examples of these methods include ImageDiff [17], ImageRegr [47],
ImageRatio [17], and Change Vector Analysis (CVA) [58]. While these methods
are simple to implement, they are not sufficient for modeling the complexity
of the problem and are heavily reliant on the threshold.

Transformation-based methods generally apply various transforms to the images
and then compute a “difference” image based on the transformed input images.
Examples of these transforms include Principal Component Analysis [34, 19],
Karhunen-Loeve Transform (KT) [17], Gramm-Schmidt (GS) [17], Multivariate
Alteration Detection (MAD) [54], Re-Weighted Multivariate Alteration Detection
(IRMAD) [53], and Chi-Square [17]. The common principle behind these methods
is that they aim to condense the important information present in the input
pair of images using transforms. With just the relevant information remaining
after the transform, they propose that a difference operation along with a
threshold can give the required segmentation map. However, these methods are
also limited by the selection of threshold as well as poor performance.

Finally, classification-based methods like Expectation-Maximization CD [17]
and spectral-temporal CD [17] use machine learning techniques to provide per-
pixel classification and perform additional post-processing to generate the
final mask. However, as compared to modern-day deep networks, these techniques
have an inferior quality.

Change Detection Using Deep Learning. Deep learning techniques produce rich
embeddings for images, allowing them to surpass traditional methods for CD.
Convolutional Neural Networks paved the way for utilization of DL for
processing the pre and post-change images and then fusing them to produce the
predicted mask. FC-EF [8] and FC-Siam-Conc [8] were one of the first methods
to do so. In FC-EF, the pre-change and post-change image are concatenated
along the channel dimension and a fully convolutional encoder-decoder
structure is applied on the joint image. In FC-Siam-Conc, the same encoder
operates on both the input images and the resulting embeddings are
concatenated before passing them through the decoder. The latter approach was
shown to be more effective in modeling the temporal difference between the two
images. Later, with the introduction of deeper backbones like VGG [59], Resnet
[27] and DenseNet [30], multiple CD techniques have evolved which improve the
performance of FC-Siam-Conac. Some examples include DT-SCN [44] and SNUNet
[21]. DT-SCN combines three subnetworks, one for change detection and two for
semantic segmentation, which adopt a ResNet backbone. SNUNet combines dense
connections and nested UNet [75] with Siamese networks for CD.

The introduction of Transformer networks for image analysis tasks in ViT [20]
was instrumental in capturing the long-range dependencies in images, which was
previously missing from CNN architectures. Since CD involves analyzing the
relation between two different images, transformer-based approaches like BIT
[11] and ChangeFormer [4] were found to be more effective than CNN methods
[11, 4]. BIT uses a combination of ResNet, followed by a transformer encoder,
that allows it to extract spatial and comparative features. Its successor, the
Changeformer uses a purely transformer architecture and showed significant
improvements over BIT. It comprises of a transformer encoder for the images as
well as a transformer decoder, which is more effective than CNNs for feature
representations.

One recent line of work uses self-supervised learning to pretrain the models
with vast amounts of unannotated data. These may be obtained from datasets
like ImageNet 1k as well as publicly available data on the internet. Some
examples of this approach include SiamSiam [15], MoCo-v2 [52], DenseCL [66],
CMC [9], SeCo [49] and SaDL-CD [10], among others. These methods usually
pretrain their encoders on self-supervision tasks, thereby vastly improving
their representative nature. For instance, SaDL-CD performs background-
swapping during training and applies data augmentations to produce three
different views of a given image. Next, it optimizes a loss function that
appropriately measures the difference between the views. SeCo, on the other
hand, enforces consistencies between features of the seasonally varying
images.

Another recent work proposes the use of a generative modeling framework called
Denoising Diffusion Probabilistic Models (DDPM) [29] as a pretraining strategy
for learning powerful encoders [3]. Once learnt, they can be used as feature
extractors to train a light-weight model for CD. Hence, these can also be
considered as a method that requires a large amount of pretraining. The
proposed approach, called DDPM-CD, outperforms all the traditional and deep
learning techniques mentioned above and is the current SOTA method for CD.

In this paper, we develop a Mamba-based mechanism that outperforms DDPM-CD as
well as other methods on four widely used public datasets for CD, showing the
effectiveness of selective state space modeling as a fundamental building
block for CD over CNNs and transformers.

Mamba in Vision Tasks. Various recent works propose the use of State Space
Modeling (S4) as an effective method of representing visual information [51,
32, 65, 33]. Trans4mer [33] and Vis4mer [32] were used to create
representations of movie clips using S4, while S4ND [51] uses S4 to
effectively model multidimensional signals in 1D, 2D and 3D. On the other
hand, S5 [65] proposes a selective mechanism for capturing long-range
dependencies in long videos, which became the motivation for Mamba [22]. Mamba
has been adapted for a multitude of computer vision tasks recently, which
shows its effectiveness in modeling visual representations. Vision Mamba [76]
proposed using the selective scan mechanism in Mamba from two directions for
an image, while VMamba [45] showed that scanning an image from four directions
has a better representation. In our work, we follow the four-directional
strategy of VMamba in the Siamese Image Encoder. Mamba has also been explored
for various applications in Computer Vision including medical image
segmentation [48, 57, 67, 70], multi-modal segmentation [64], point cloud
computations [41], and image restoration [25]. However, most of these methods
involve direct replacement of transformer blocks with Mamba blocks, without
adapting for the particular task. Unlike this plug-and-play approach, we
develop a Mamba-based difference module and decoder that is tailored for the
task of CD.

Concurrent Works in Mamba for CD. A couple of recent CD methods have proposed
using Mamba as the backbone architecture. While working on the proposed
method, we recognize these recent methods as concurrent works and compare our
method with them. ChangeMamba [14] uses three different designs in the decoder
for modeling the spatio-temporal relationship between the pre-change and post-
change images. RSMamba [74], on the other hand increases the number of
scanning directions in VMama from four to eight in order to capture relations
from all directions. In contrast, our method uses a single decoder design and
the standard four directions for scanning. Instead, we design the decoder to
use features at multiple scales for spatial learning and design a separate
difference module to learn temporal relations. CDMamba [73] comes close to our
proposed method in that it uses multi-scale features in the decoder. However,
our method also additionally uses a difference module to allow the model to
learn temporal relations between the two images better. Experimental results
on multiple datasets show that our method outperforms concurrent Mamba-based
methods showing the significance of our architecture design for CD.

##  3 Proposed Method

###  3.1 Preliminaries

State Space Modeling.  State Space Models (SSM) are a class of sequence-to-
sequence modeling algorithms, which follow the linear time invariance (LTI)
property [23, 24, 60]. These systems work by maintaining a hidden state that
changes with time. The output of the system at any point in time is dependent
on the hidden state as well as the input. More specifically, given input
$x(t)\in\mathbb{R}$, the following equations define the output
$y(t)\in\mathbb{R}$ and the hidden state $h(t)\in\mathbb{R}^{N}$, where $N$ is
the size of the hidden state:

$y(t)=Ch(t)+Dx(t),\;\;\;\dot{h}(t)=Ah(t)+Bx(t),$ (1)

where $\dot{h}(t)$ denotes the first derivative with respect to time $t$. The
matrices $A\in\mathbb{R}^{NXN}$, $B\in\mathbb{R}^{NX1}$,
$C\in\mathbb{R}^{1XN}$, and $D\in\mathbb{R}$ are characteristic to the system
and are learnt during training. Further, to model discrete sequences like
images, SSMs utilize a predefined discretization parameter $\Delta$, which
maps the parameters $A$ and $B$ to the discrete space. This technique is known
as Zero Order Hold Discretization [23]. More specifically, the discretization
process is defined as follows:

$y_{k}=\overline{C}h_{k}+\overline{D}x_{k},\;\;h_{k}=\overline{A}h_{k-1}+\overline{B}x_{k},$
(2)

where $\\{x_{1},x_{2},...,x_{k}\\}$ denotes the discrete input sequence,
$\\{y_{1},y_{2},...,y_{k}\\}$ denotes the output sequence, and the SSM
matrices $\overline{A},\overline{B},\overline{C},\overline{D}$ are defined as
follows:

$\displaystyle\overline{A}=exp(\Delta A),\;\;\overline{B}=(\Delta
A)^{-1}(exp(A)-I)(\Delta B)$ $\displaystyle\overline{C}=C,\;\;\overline{D}=D.$
(3)

Figure 2: An overview of the M-CD Architecture. Given a pre-change and post-
change image, they are passed through a Mamba-based encoder with shared
weights (Siamese) and features at multiple scales are extracted. The
Difference Module combines them before sending to the Mask Decoder, which uses
a Mamba-based Decoder to generate the predicted change map.

Selective Scan Modification. The LTI property of SSMs causes them to learn a
general set of parameters that are expected to work well for all kinds of
inputs. Mamba (Selective State Space Model) [22] addresses this concern by
making the parameters input-dependent and shows that this allows the model to
perform significantly better. More specifically, the parameters $B,C$ and
$\Delta$ are dependent on the input $x$ during training, making Mamba capable
of effectively learning the complex relations in longer sequences. In
addition, Mamba also approximates $\overline{B}\simeq\Delta B$ using Taylor
approximation.

###  3.2 M-CD Architecture

An overview of the proposed M-CD architecture is shown in Figure Fig. 2. M-CD
consists of three main components - the Siamese Image Encoder (SIE), the
Difference Module (DM) and the Mask Decoder (MD). Given two images, they are
passed through the two branches of the encoder to generate image features. The
two branches work on the same modality of images and so weights between them
are shared. This also reduces the computational complexity. The SIE is
responsible for extracting features on multiple scales, facilitated by the
cascading of four Visual State Space (VSS) blocks and downsampling operations.
The DM is responsible for analyzing features from both images together at
different scales and generating combined multi-scale features. These are
further transformed by the Mask Decoder using Channel-Averaged VSS blocks and
upsampling operations. These transformed features are finally passed through a
classifier that segments out the regions involving significant change. In the
subsequent subsections, we describe each of the three components of M-CD in
greater detail.

###  3.3 Siamese Image Encoder (SIE)

The SIE has two branches with common weights, each of which takes an RGB
image. One of these is the pre-change image and the other is the post-change
image. These images go through a stem module which is a series of 2D
convolution layers to extract preliminary features, similar to Vision
Transformer (ViT) [20]. This is followed by the VSS block for further
processing. As seen in Fig. 3, the VSS block consists of a linear layer,
followed by a Depth-wise Convolution layer, similar to the original Mamba
[22]. This is followed by the Selective Scan 2D (SS2D) Module, similar to
VMamba [45] and MambaIR [25]. As shown in Fig. 3, The SS2D module flattens the
input and processes it from four different directions - top-left to bottom-
right, bottom-right to top-left, top-right to bottom-left, and bottom-left to
top-right. This is done to extract long-range dependencies from multiple
directions, as suggested by VMamba [45]. For each of the directions, the
selective scan state model is learnt by learning the parameters
$A,B,C,D,\Delta$, and the results are merged to produce the final output of
the SS2D block. Lastly, the VSS block consists of a layernorm operation
followed by a linear operator. The SIE consists of four such VSS blocks. After
the first block, each of the VSS blocks are preceeded by a downsampling
module. Thus, the SIE produces deep multi-scale features as the outputs of
each of the VSS blocks for both the input images.

Figure 3: Architecture of Visual State Space Block and Selective Scan 2D
block.

###  3.4 Difference Module (DM)

As seen in Fig. 4, the difference module takes in features corresponding to
the two images (pre-change and post-change), and outputs one feature vector to
be used by the mask decoder. Each of the input features passes through a
linear and a depth-wise convolution operation, followed by a Joint Selective
Scan (JSS) module. The JSS module concatenates the outputs from the previous
layer in two ways - $Pre;Post$ and $Post;Pre$, where $Pre$ denotes the pre-
change image, $Post$ denotes the post-change image, and ; represents
concatenation. The selective scan operations are performed on these two
concatenated features and the outputs are added. Unlike transformers which use
self-attention to attend to each token, the DM uses a scanning operation.
Hence, it is important to concatenate in both directions to maintain symmetry
of the network and aid the training process. The resulting vector is then
split into the pre-image and post-image parts and passed through a layernorm
operation. The linear and convolution operations are used to refine the
incoming features, while the JSS module is used for learning to identify the
significant differences between the two images. In addition, a residual
connection is added between the features before the convolution and after the
normalization layer to aid training by reducing the effect of vanishing
gradients. The resulting vectors are concatenated and passed through a final
linear layer, before passing the result to the mask decoder. For capturing the
difference information between images at different scales, we employ four
separate DMs corresponding to four different scaled inputs from the image
encoder.

Figure 4: Architecture of the Difference Module and Joint Selective Scan.

###  3.5 Mask Decoder (MD)

As shown in Fig. 2, the Mask Decoder is responsible for generating the output
mask. The Mask decoder consists of a series of Channel-Averaged VSS (CAVSS)
blocks and upsampling operations. The architecture of a CAVSS block is shown
in Fig. 5. It has a VSS block in the beginning that processes the output from
the Difference Module. However, while the VSS block can extract global context
well, it struggles to learn the inter-channel dependencies [64]. Hence, the
CAVSS additionally contains average-pool and max-pool operations along the
channel dimension, that cater to the channel information, similar to CBAM
[68]. Thus, the VSS and the Channel-Averaged Pooling layers learn the spatial
as well as the channel context. The Mask Decoder follows a UNet [56]
structure, where the output of a Difference Module passes through the CAVSS
block, is upsampled and added to the output of the previous Difference Module.
Adding these skip connections allows the model to benefit from a wider context
and reduce the effect of vanishing gradients. Finally, the output from the
last CAVSS block is upsampled to the original image size and fed into a
classifier that classifies each pixel as “Change” or “No Change” classes.

Figure 5: Architecture of Channel Averaged VSS Block

##  4 Experiments and Results

Datasets: We compare our method with current state-of-the-art (SOTA) methods
for change detection on four widely used public datasets: WHU-CD [35], DSIFN-
CD [72], LEVIR-CD [13], and CDD [40]. For all the datasets, we use the same
preprocessing and data splits as [4]. WHU-CD consists of paired aerial images
from 2012 and 2016. It represents an earthquake-affected area, with many
changes including rebuilt and new buildings. The training set consists of 5947
image pairs, while the validation set and test set have 743 and 744 pairs
respectively. DSIFN-CD [72] has images from six cities in China from different
times. The training, validation and test sets have 14400, 1360, and 192 image
pairs respectively. LEVIR-CD [13] consists of remote sensing images, collected
from Google Earth and majorly covers building appearances and disappearances.
It has 7120 image pairs in training set, 1024 in validation set and 2048 in
the testing set. CDD [40] consists of Google Earth remote sensing images from
varying seasons. It caters to changes in large architectural structures, cars,
and seasonal changes in natural vegetation. All datasets consist of high-
resolution images and the data splits were created using $256\times 256$ crops
from the original images, and obtained from [4].

Experimental Setup: We use the AdamW optimizer [46] with initial learning rate
$6e^{-5}$ and weight decay $0.01$. The batch size during training is kept at
$8$ and the model is trained for $150$ epochs. For initialization of the SIE,
we use the ImageNet 1k pretrained model VMamba-Small [45]. The training is
divided among four Nvidia RTX A5000 GPUs, each taking 6GB memory, while just
one of these is used during evaluation. Following [3], we use three metrics
for evaluation - F1 score, Intersection-Over-Union (IoU) and Overall Accuracy
(OA).

Results:  The quantitative results comparing M-CD with existing SOTA methods
are tabulated in Tab. 1. The first three rows in the table represent methods
that employ CNNs for CD. Among these, SNUNet [21] and FC-Siam-conc [8] start
training with random weight initialization, while DT-SCN [44] and IFNet [71]
use pretrained ImageNet 1k weights for initializing the models. While
convolution operations are useful in capturing the spatial context of an
image, they lack in comparing the temporal differences between the two images
in CD. In contrast, STANet [12] uses spatial and temporal attention, while BIT
[11] and ChangeFormer [4] adapt transformers and employ cross-attention
operations between the two images, which significantly improves results over
convolution-based methods.

Other recent methods like SiamSiam [15] and SaDL-CD [10] require a large
amount of pertaining and hence, we only present the results which were
mentioned in their original papers, namely the F1 score and IoU score on the
WHU-CD and LEVIR-CD datasets. We find that our method is able to significantly
outperform all the self-supervision-based approaches without requiring a large
amount of data. This shows the effectiveness of the Mamaba-based modeling over
strong pretrained encoders. DDPM-CD [3] instead learns a diffusion model to
learn the structure of remote sensing images and uses its encoder to generate
strong predictions, thus being the current SOTA for CD.

On the other hand, it was shown that Selective State Space Models like Mamba
are capable of learning through a larger receptive field as compared to
convolutions or transformer-based mechanisms [22, 45]. This can be seen
through the on-par performance of Mamba-based methods like RSMamba [74],
CDMamba [73] and ChangeMamba [14]. However, we see that they do not improve
the performance significantly. In contrast, by using multi-scale features and
a dedicated module to model the temporal difference, we see that our method
M-CD significantly improves over existing methods. M-CD is able to outperform
CNN-based methods by around 7-10% in IoU score, and outperform transformer-
based methods by around 5%. In addition, it also performs better than DDPM-CD
and Mamba-based methods, with a rise of around 3% in IoU score. At the same
time, our method is also initialized with ImageNet 1k weights and so it does
not require a large amount of pretraining with remote sensing image data,
unlike DDPM-CD. All results with M-CD have a p-value of at most $10^{-5}$
compared to other methods, indicating statistical significance.

We present the qualitative results of our method in Fig. 6. We see that while
DDPM-CD generates some artefacts is the output masks, M-CD generates cleaner
segmentation predictions, with more precise boundaries.

|  | WHU-CD [35] | DSIFN-CD [72] | LEVIR-CD [13] | CDD [40]
---|---|---|---|---|---
Method | Extra training data | F1 ($\uparrow$) | IoU ($\uparrow$) | OA ($\uparrow$) | F1 ($\uparrow$) | IoU ($\uparrow$) | OA ($\uparrow$) | F1 ($\uparrow$) | IoU ($\uparrow$) | OA ($\uparrow$) | F1 ($\uparrow$) | IoU ($\uparrow$) | OA ($\uparrow$)
CNN-based Methods: |
FC-Siam-conc [8] | None | 0.798 | 0.665 | 98.5 | 0.597 | 0.426 | 87.6 | 0.837 | 0.720 | 98.5 | 0.751 | 0.601 | 94.9
SNUNet [21] | None | 0.835 | 0.717 | 98.7 | 0.662 | 0.495 | 87.3 | 0.882 | 0.788 | 98.8 | 0.839 | 0.721 | 96.2
IFNet [71] | IN1k | 0.834 | 0.715 | 98.8 | 0.601 | 0.430 | 87.8 | 0.881 | 0.788 | 98.9 | 0.840 | 0.719 | 96.03
CNN + Attention based Methods: |
DT-SCN [44] | IN1k | 0.914 | 0.842 | 99.3 | 0.706 | 0.545 | 82.9 | 0.877 | 0.781 | 98.8 | 0.921 | 0.853 | 98.2
STANet [12] | IN1k | 0.823 | 0.700 | 98.5 | 0.645 | 0.478 | 88.5 | 0.873 | 0.774 | 98.7 | 0.841 | 0.722 | 96.1
Transformer-based Methods: |
BIT [11] | IN1k | 0.905 | 0.834 | 99.3 | 0.876 | 0.780 | 92.3 | 0.893 | 0.807 | 98.92 | 0.889 | 0.800 | 97.5
ChangeFormer [4] | None | 0.886 | 0.795 | 99.12 | 0.947 | 0.887 | 93.2 | 0.904 | 0.825 | 99.0 | 0.946 | 0.898 | 98.7
Methods with self supervised pretraining |
SiamSiam [15] | IN1k, IBSD, Google Earth | 0.847 | 0.734 | - | - | - | - | 0.880 | 0.786 | - | - | - | -
MoCo-v2 [52] | IN1k, IBSD, Google Earth | 0.882 | 0.789 | - | - | - | - | 0.879 | 0.784 | - | - | - | -
DenseCL [66] | IN1k, IBSD, Google Earth | 0.867 | 0.765 | - | - | - | - | 0.869 | 0.780 | - | - | - | -
CMC [9] | IN1k, IBSD, Google Earth | 0.886 | 0.795 | - | - | - | - | 0.877 | 0.780 | - | - | - | -
SeCo [49] | IN1k, IBSD, Google Earth | 0.883 | 0.790 | - | - | - | - | 0.881 | 0.787 | - | - | - | -
SaDL-CD [10] | IN1k, IBSD, Google Earth | 0.909 | 0.833 | - | - | - | - | 0.899 | 0.818 | - | - | - | -
Mamba-based Methods |
RSMamba [74] | IN1k | 0.927 | 0.865 | 99.4 | 0.965 | 0.913 | 97.0 | 0.897 | 0.814 | 98.9 | 0.943 | 0.902 | 98.8
ChangeMamba [14] | IN1k | 0.925 | 0.861 | 99.4 | 0.875 | 0.778 | 95.8 | 90.16 | 0.821 | 99.0 | 0.944 | 0.920 | 99.0
CDMamba [73] | IN1k | 0.937 | 0.882 | 99.5 | 0.966 | 0.914 | 97.0 | 0.907 | 0.831 | 99.0 | 0.960 | 0.919 | 99.1
Diffusion-based Methods |
DDPM-CD [3] | Google Earth | 0.927 | 0.863 | 99.4 | 0.967 | 0.913 | 97.1 | 0.909 | 0.833 | 99.1 | 0.956 | 0.916 | 99.0
M-CD (Ours) | IN1k | 0.953 | 0.911 | 99.6 | 0.970 | 0.935 | 98.9 | 0.921 | 0.850 | 99.2 | 0.982 | 0.963 | 99.5

Table 1: Comparison of M-CD with respect to SOTA CD methods. F1 denotes the F1
metric, IoU denotes the Intersection-Over-Union metric and OA denotes the
overall pixel accuracy. IN1k denotes training data from ImageNet 1k dataset
[18]. The best result is indicated in bold and the second-best result is
underlined. Our method outperforms existing methods for all datasets. Figure
6: Qualitative results on four public datasets. White represents true
positives, black represents true negatives, green represents false positives
and red represents false negatives.

##  5 Ablation Studies

Varying Mamba Backbones: In our main experiments, we use the VMamba-Small
backbone in the SIE. VMamba [45] provides three different model settings,
namely Vmamba Tiny, Small and Base in the increasing order of complexity. We
measure the effect of the choice of the backbone for our task by comparing the
results on LEVIR-CD in Tab. 2. We find that while there is no significant
difference in the performance, VMamba-Small performs slightly better. One
reason for this could be that the number of parameters from VMamba-Small to
Base increase by 70%, which can be more prone to overfitting. Hence, there is
no significant improvement, when going from Small to Tiny. A similar argument
can also hold while going from Tiny to Small.

Backbone | Number of Parameters | LEVIR-CD IoU
---|---|---
Tiny | 36.5 M | 0.848
Small | 58.1 M | 0.850
Base | 100.7 M | 0.845

Table 2: Ablation Experiments with varying encoder backbones. While the number
of parameters increase significantly, there is no significant change in model
performance on LEVIR-CD.

Varying Difference Module: We measure the effect of the design choice of the
Difference Module (DM) on model performance for LEVIR-CD and DSIFN-CD datasets
in Tab. 3. We evaluate three different designs for the Difference Module. In
the first row, “Difference” signifies that the DM simply subtracts the post-
change image from the pre-change image and sends the difference to the
decoder. While this approach seems naive, this allows the decoder to learn
from the residuals between the image features and performs well on the Change
Detection task. In the second row, we use the strategy of combining of Cross
Mamba and Concat Mamba blocks used by Sigma [64] to generate segmentation
masks using multiple modalities. This technique performs well when multiple
modalities guide the model towards a single segmentation results. However, for
the case of Change Detection, we find that interchanging the parameters for
the two images during training, as proposed by Sigma leads to a lower model
performance. This could be because an interchange encourages the model to
treat the post-change image as being similar to the pre-change image
semantically. This proves to be beneficial when fusing two modalities like in
Sigma, but is harmful for the task of Change Detection. Hence, we see a lower
performance. Finally, the last row represents our method, which uses the
concatenation of the feature vectors from the two images. This allows the DM
to combine the two features with more freedom than simple difference, while
not enforcing similarity. Hence, it achieves a higher performance than the
other two strategies.

Difference Module Design Strategy | LEVIR-CD IoU | DSIFN-CD IoU
---|---|---
Difference | 0.840 | 0.924
Cross+Concatenation [64] | 0.821 | 0.909
Concatenation (Ours) | 0.850 | 0.935

Table 3: Ablation Experiments with varying DM designs. Our method
(concatenation) shows higher performance on the LEVIR-CD and DSIFN-CD datasets
over other strategies like difference of features, or combining parameter
crossing and concatenation, as proposed in [64].

Computational Complexity Analysis: We calculate the compute required for
running our method using three metrics, namely the number of trainable
parameters the model has, amount of floating point operations required for a
forward pass, in gigaflops (GFLOPS) and the average inference time required
for one pair of pre and post-change image inputs. The image size for these
experiments is 256X256 and one Nvidia RTX A5000 GPU is used for the
calculation of these metrics. The results are tabulated in Tab. 4. We see that
our method has a greater number of trainable parameters as well as has a
higher inference time as compared to other SOTA methods. However, GFLOPS in
Mamba-based architectures have a linear scaling relation with the number of
parameters [22]. Hence, the number of floating point operations are comparable
to other methods and significantly lower than DDPM-CD. For rest of the
methods, while the computational complexity of our method is greater, the gain
in performance, especially in the absence of large amounts of pertaining,
outweighs this limitation in our opinion.

Method | Trainable Parameters (million) | GFLOPS | Average Inference Time Per Image Pair (ms)
---|---|---|---
SiamSiam | 12.49 | 4.76 | 1.04
MoCo-v2 | 11.24 | 4.76 | 1.92
DenseCL | 11.69 | 4.76 | 2.66
CMC | 22.48 | 4.66 | 1.55
SeCo | 12.16 | 9.52 | 3.62
DDPM-CD [3] | 46.41 | 2175.46 | 88.10
M-CD (Ours) | 69.80 | 29.58 | 160

Table 4: Computational Complexity Analysis of M-CD. Our method has greater
trainable parameters and inference time. The number of GFLOPs are comparable.
However, while this is a limitation of M-CD, we believe the improvement in
performance without additional training data outweighs the shortcomings.
Figure 7: Effective Receptive Field (ERF) of SNUNet v.s. ChangeFormer v.s.
M-CD for WHU dataset. The top row indicates ERF with respect to the pre-change
image (A) and the bottom row indicates ERF with respect to the post-change
image (B). Darker shade of green indicates higher dependence on the input
pixel. M-CD and Transformer have a more global ERF than SNUNet. M-CD has a
more structured ERF than the uniform ERF of ChangeFormer

Effective Receptive Field (ERF): We utilize the code provided by VMamba [45]
to generate the ERF for our method (Mamba-based) in comparison to ChangeFormer
[4] (transformer-based) and SNUNet [21] (CNN-based). This represents the
importance of every input image pixel for generating the center of the
predicted mask. As shown in Fig. 7, using Mamba architecture includes
information from both the input images at a more global level compared to
CNNs, as seen from the darker shades of green at non-centric parts of the ERF.
On the other hand, the transformer has a uniformly distributed attention map
unlike Mamba, which is more structured. This shows that Mamba can achieve a
wider receptive field across both the pre and post-change images, and can find
and prioritize certain areas of the inputs which are more relevant for CD.
These results are consistent with similar studies conducted in [45].

##  6 Conclusion

In this work, we introduce M-CD, a model for the task of Change Detection,
that is based upon the Selective State Space (a.k.a. Mamba) architecture. M-CD
comprises of a Siamese Image Encoder to featurize the input images, a
Difference Module that performs multi-scale analysis of the change of
interest, and finally, a Mask Decoder to generate the segmentation masks.
Extensive experiments on four widely used datasets clearly show that M-CD
beats the existing SOTA methods. At the same time, it requires much lesser
pretaining than the recently proposed self-supervised or diffusion methods,
while outperforming them significantly, thus making M-CD the new SOTA in this
field.

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