#  CoVid-19 Detection leveraging Vision Transformers and Explainable AI

Pangoth Santhosh Kumar  Data Science and Artificial Intelligence
IIIT Naya Raipur
<EMAIL_ADDRESS>Kundrapu Supriya  Computer Science and Engineering
IIIT Naya Raipur
<EMAIL_ADDRESS>Mallikharjuna Rao K  Data Science and Artificial
Intelligence
IIIT Naya Raipur
<EMAIL_ADDRESS>
###### Abstract

Lung disease is a common health problem in many parts of the world. It is a
significant risk to people’s health and quality of life all across the globe
since it is responsible for five of the top thirty leading causes of death.
Among them are COVID-19, pneumonia, and tuberculosis, to name just a few. It
is critical to diagnose lung diseases in their early stages. Several different
models including machine learning and image processing have been developed for
this purpose. The earlier a condition is diagnosed, the better the patient’s
chances of making a full recovery and surviving into the long term. Thanks to
deep learning algorithms, there is significant promise for the autonomous,
rapid, and accurate identification of lung diseases based on medical imaging.
Several different deep learning strategies, including convolutional neural
networks (CNN), vanilla neural networks, visual geometry group-based networks
(VGG), and capsule networks, are used for the goal of making lung disease
forecasts. The standard CNN has a poor performance when dealing with rotated,
tilted, or other aberrant picture orientations. As a result of this, within
the scope of this study, we have suggested a vision-transformer-based approach
end to end framework for the diagnosis of lung disorders. In the architecture,
data augmentation, training of the suggested models, and evaluation of the
models are all included. For the purpose of detecting lung diseases such as
pneumonia, Covid-19, lung opacity, and others, a specialised Compact
Convolution Transformers (CCT) model have been tested and evaluated on
datasets such as the Covid-19 Radiography Database. The model has achieved a
better accuracy for both its training and validation purposes on the Covid-19
Radiography Database . There have been a number of different evaluation
criteria utilised, such as accuracy, recall, confusion matrix, and so on. been
used for the purpose of analysing the model. And than these we also used XAI
for evaluationg the model.

##  I Introduction

Infections of the lungs are persistent illnesses that have an effect on the
human body’s tissues and organs and make it difficult to breathe.A few
examples of lung diseases include pneumonia, Covid-19, tuberculosis, lung
cancer, and other other lung problems. According to the Forum of International
Respiratory Societies [1], approximately 334 million people around the world
suffer from asthma. Additionally, each year 1.4 million people pass away as a
result of tuberculosis, 1.6 million pass away as a result of lung cancer, and
millions more pass away as a result of pneumonia. The COVID-19 pandemic
resulted in the infection of millions of individuals, which in turn had a
detrimental impact on the healthcare systems located across [2]. There is no
question that disorders affecting the lungs are among the leading causes of
death and disability across the globe. Early detection considerably boosts
both the patient’s chances of making a full recovery and their chances of
surviving for a longer period of time [3]. Healthcare informatics is the field
that deals with the management and use of information in the healthcare
industry. One example of how machine learning and deep learning algorithms can
be used in healthcare informatics is in the detection of lung diseases, such
as pneumonia. The widespread distribution of COVID-19 has increased interest
in the early detection of lung diseases, as the virus can cause severe lung
damage and respiratory issues [4]. In addition, other viral or bacterial
infections can also contribute to pneumonia, a type of lung disease. Two
commonly used diagnostic methods for finding lung disorders are computed
tomography (CT) scans and CXR imaging [5]. CT scans use X-rays to produce
detailed images of the inside of the body, while CXR imaging uses a special
camera to produce images of the chest. These diagnostic methods can be used to
identify abnormalities in the lungs that may indicate the presence of a lung
disease.

Figure 1: The functionality of the proposed solution

On the other hand, medical imaging treatments like X-ray and CT-based
screening are more often used since they are more readily accessible, they are
quicker, and they are typically safe. Imaging using X-rays, as opposed to
imaging with CT, is often used for COVID-19 screening since it needs less
imaging time and is less expensive than CT imaging. Scanners that use X-ray
technology are frequently available even in more remote locations. In recent
years, academics have focused their attention on developing methods for
diagnosing lung diseases using a variety of methods, including classical
machine learning and deep learning. CNN, VGGNet, ResNet, and LSTM are among
the many algorithms that may be used in the process of diagnosing lung
disorders. In this study, we suggest a transformer-based architecture for the
classification and diagnosis of lung diseases. The following is a list of the
significant contributions that this work makes:

  * •

The use of CT Scan and CXR datasets for the purpose of training the
transformer models.

  * •

Constructed and analysed the sophisticated image classification model with
transformers for the identification of lung disease.

  * •

Conducting an analysis of the trained models by employing a variety of
accuracy measures, such as precision and recall, amongst others.

##  II Related Works

Several computer-aided design (CAD) systems have been developed throughout the
years to assist physicians in deciphering medical images [6]. However,
developing a reliable CAD system is challenging. More choices in how to build
such a system have emerged because to the development of powerful graphics
processing units (GPUs) and deep learning approaches like convolutional neural
networks (CNNs). Chest X-rays, CT scans, histopathology images, etc. may all
be used in conjunction with deep learning methods to diagnose lung disorders.
Briefly summarise the latest studies that have been undertaken by various
researchers to diagnose lung disease utilising chest x-ray and CT scan
pictures in the next two subsections.

###  II-A X-Ray Imaging for the Diagnosis of Lung Disease

X-ray pictures provide a number of challenges for clinical interpretation,
including various possible abnormalities and complicated backgrounds[7]. This
calls for expert-level manual annotation (radiologists). The ability to
automatically analyse X-ray images is rapidly becoming into an important
diagnostic resource. Since recent advancements in this area [8] have made deep
neural networks widely used, they are often used to the problem of X-ray
picture categorization. When compared to other pneumonias, COVID-Net [9] is
the only open-source, created, and maintained technology that can reliably
identify between COVID-19 and other pneumonias. From the provided specs and
preliminary design prototype, COVID-Net is able to learn the architectural
design starting point and go further with machine-driven design exploration.
Your chest X-ray will work with it. Classification as ”normal,” ”pneumonia,”
or ”COVID-19.” Nahid et al [10] .’s method varied the gathering of
discriminative features by fusing image processing techniques with a two-
channel CNN. The model correctly identified cases of pneumonia with a
sensitivity of 97.92%. If done by hand, however, the crop operation needed to
remove the unwanted components would be time-consuming and difficult. In [11],
a CNN model that makes use of GradCAM to draw attention to affected regions is
shown. The model has a good validation accuracy of 84.8%. However, the authors
did not use any augmentation methods to produce unique training samples; as a
consequence, the samples were identical. In order to construct models for
medical imaging, transfer learning is often used as a workaround for the
scarcity of available training data. Chouhan et al. [12] employed a transfer
learning technique to classify X-rays of pneumonia. They pooled the knowledge
of five trained models to make pneumonia diagnosis more accurate. To diagnose
pneumonia, Rahman et al. [13] used a transfer learning strategy based on
convolutional neural networks. We employed models such as AlexNet, ResNet18,
DenseNet201, and SqueezeNet to identify chest X-rays that showed germs,
viruses, or were just normal. The accuracy rate after using their approach was
98

###  II-B CT scan for the Diagnosis of Lung Disease

Roy, Sirohi, and Patle [14] developed a method for detecting lung cancer
nodules using a due to uncontrollable system as well as an adaptive
thresholding model. Using grey transformation, this method boosts contrast in
the visual field. Segmentation is performed using an active contour model once
an image has been binarized. The diagnosis of cancer is categorised using
fuzzy inference. Features are retrieved for use in training the classifier,
and they include the area, mean, entropy, correlation, main axis length, and
minor axis length. The total accuracy of the system is 94.12%. Given its
constraints, the suggested model cannot be used to differentiate between
benign and malignant tumours. The K mean unsupervised learning method is used
by Sangamithra and Govindaraju [15] for classification or clustering purposes.
It classifies the dataset of pixels according to several characteristics. In
order to classify data, this model employs a back propagation neural network.
Features including entropy, correlation, homogeneity, PSNR, and SSIM may be
retrieved via the gray-level co-occurrence matrix (GLCM) method. Roughly 90.7%
accuracy may be expected from the system. Using a median filter, which is
often used for picture improvement, may aid our new model in filtering out
noise and improving accuracy.

##  III Research Methodology

In this subsection, we will talk about our suggested unique architecture for
defect diagnosis of X-Ray and CT Scan pictures. This design primarily consists
of three phases: image pre-processing, image augmentation, and our Vision
Transformers architecture for automatically extracting ROIs, classifying
images and identification of diseases.

Figure 2: The Overview of Proposed Solution

Figure 3: The Ben Graham Processing and Data Augmentation of the images

###  III-A Contrast limited adaptive histogram equalization

X-Ray and CT-Scan photographs used in the medical field often include noise
from the medical field, such as noise in the backdrop that is hazy. The poor
performance of the prediction models may be attributed to the presence of
noise in an X-Ray picture. Aside from the background noise, it is vital to
extract the area of interest (ROI) in order to improve the performance of
predictive algorithms, hence minimizing the amount of redundant information
and the amount of work required for computational pattern recognition. The
vast majority of the present architectural designs do not take these critical
aspects into account; instead, they force unfiltered imagery to fit into a
predicted framework. Taking all of these considerations into account, the
approach that has been suggested improves the quality of the picture and
derives the ROI by contrast-limited adaptive histogram equalization (CLAHE).
This strategy, in contrast to the designs that are now in use, lessens the
amount of computing work required and boosts the predictive models’ overall
performance. The issue of contrast over-amplification is addressed by CLAHE,
which is a modification of the technique known as adaptive histogram
equalization (AHE). CLAHE works with discrete parts of a picture, which are
referred to as tiles, rather than analyzing the whole image. Tiles that are
next to one another are blended together using bilinear interpolation in order
to get rid of arbitrary boundaries.

###  III-B Ben Graham Method:

Contrast CLAHE, or limited adaptive histogram equalization, is used with Ben
Graham’s preprocessing approach, which involves eliminating the colour that is
considered to be the local average. When compared to utilizing either of the
algorithms on its own, this results in an improvement in the clarity of the
vessels in the majority of the photos. However, after conducting a number of
trials, it was shown that the performance of the combination of CLAHE and Ben
Graham’s method is marginally worse than that of CLAHE when applied to certain
circumstances, particularly the Chase dataset. In order to address this issue,
a feature fusion method is used, which consists of concatenating the solitary
CLAHE preprocessed picture with the combined CLAHE and Ben Graham’s
preprocessed image. This method ranks highest among all of the tests that were
carried out on all three datasets.

###  III-C Data Augmentation

Because more convolutional networks do not have inductive biases, ViT has only
been validated as a benchmark design if it is trained on big datasets. This is
because more convolutional networks do not have inductive biases. In this
study, we adapted the idea from the analysis presented by Steiner et al., and
the approach we suggested provides image augmentation to ViT-based designs in
a cautious manner, resulting in higher efficiency. In addition to this, the
relevance of ViT frameworks may be increased by the visual alternatives that
are provided through augmentation. As a result, the pictures of the ROI that
were extracted are subjected to further processing, during which they are
altered visually in a variety of ways using image enhancement methods such as
gaussian blur, random rotation, zooming, and flipping in a variety of
orientations.

###  III-D Compact Convolution Transformers

The convolutional block in CCT allows the model to capture spatial
relationships between patches of input data, which can be useful for tasks
such as image classification. This is because a convolutional layer is able to
learn local patterns in the data and is able to take into account the spatial
structure of the input. By using a max pooling layer after the convolution,
the model is able to reduce the dimensionality of the output while maintaining
the most important information. The ReLU activation function helps the model
learn non-linear relationships between the input and the output. Overall, the
use of the convolutional block in CCT can improve the model’s performance on
certain tasks compared to the patch and embedding approach used in ViT.

The convolutional block in CCT is applied to the feature map of an input
image. The feature map, represented by L, is a representation of the input
image that encodes important information such as edges and textures. In the
convolutional block, the feature map is first passed through a Conv2d
operation with D filters, where D is the embedding dimension of the
transformer backbone. This operation applies a set of D filters to the feature
map, each of which is able to learn a different pattern in the data. Next, the
output of the Conv2d operation is passed through a max pooling layer. This
layer takes the maximum value of each filter output, which helps reduce the
dimensionality of the output while preserving the most important information.
The output of the max pooling layer is then passed through a ReLU activation
function, which helps the model learn non-linear relationships between the
input and the output.

$Image(L)\in\mathbb{R}^{H\times W\times C}$ (1)

$L_{0}=MaxPool(ReLU(Conv2D(L))$ (2)

Overall, the use of the convolutional block in CCT allows the model to capture
spatial relationships between patches of input data and improve its
performance on tasks such as image classification. This is because the
convolutional block is able to learn local patterns in the data and take into
account the spatial structure of the input, which is useful for these types of
tasks.

Incorporating a convolutional phase in CCT allows the technique to be more
flexible and adaptable than techniques like ViT. This is because the
convolutional phase allows the model to process input images of any
resolution, rather than being limited to input images that are exactly divided
by the patch size, as is the case with ViT. The use of convolutional blocks in
CCT is also more effective in producing tokens for the transformer. Because
convolutional blocks are able to learn local patterns in the data, they are
able to generate tokens that better capture the information in the input
image. This can be useful for tasks such as image classification, where the
model needs to be able to extract important features from the input image in
order to make accurate predictions. Furthermore, the convolutional blocks in
CCT can be repeated for additional downsampling, which allows the model to
learn hierarchical representations of the input data. This can improve the
model’s performance on certain tasks, as it allows the model to learn both
local and global patterns in the data. Additionally, the number of
convolutional blocks and the downsampling ratio can be adjusted to suit the
specific needs of the task at hand.

Figure 4: Data Distribution with respect to mean, max and min value

In ViT, the transformer is used with class tokenization, which involves
dividing the input data into patches and treating each patch as a separate
token [16]. In CCT, the transformer is used with sequence pooling, which is an
attention-based technique that pools information from the entire output token
sequence. This is different from class tokenization, as it retains information
from the entire sequence rather than just individual patches. The use of
sequence pooling in CCT is motivated by the fact that the output token
sequence contains important information that spans multiple regions of the
input data. Retaining this information can improve the model’s performance on
certain tasks. Additionally, using sequence pooling reduces the number of
tokens that need to be processed by the transformer, which can slightly reduce
the computation required for the model. Overall, the use of sequence pooling
in CCT can improve the model’s performance and make it more efficient compared
to using class tokenization in ViT as represented in [17]. The output sequence
is mapped in this operation using the transformation $T:\mathbb{R}^{B\times
L\times D}$

$Z_{S}=f(L_{0})\in\mathbb{R}^{B\times L\times D}$ (3)

In CCT, the output of the transformer encoder, represented by $Z_{S}$, is
passed through a linear layer $g(Z_{S})$. The linear layer, represented by
$g(Z_{S})$, maps the output of the transformer encoder to a vector of size
$D\times 1$, where D is the total embedded dimension of the input data. The
transformer encoder architecture of CCT is inspired by [18]. This vector is
then passed through a softmax activation function, which produces a
probability distribution over the possible classes of the input data. The use
of the linear layer and the softmax activation function in CCT allows the
model to make predictions about the class of the input data. The linear layer
maps the output of the transformer encoder to a vector of size $D\times 1$,
which encodes important information about the input data. The softmax
activation function then converts this vector into a probability distribution
over the possible classes, which allows the model to make a prediction about
the class of the input data. Overall, this process allows CCT to effectively
classify input data.

${Z}^{`}_{S}=softmax({g(Z_{S})^{T}})\in\mathbb{R}^{D\times 1\times n}$ (4)

$Z={Z_{S}}{Z}^{`}_{S}=softmax({g(Z_{S})^{T}})\times{Z_{S}}\in\mathbb{R}^{B\times
1\times D}$ (5)

Equation 10 in CCT generates an output for each input token, which is then
processed by equation 11. By flattening the output, $Z\in\mathbb{R}^{B\times
1\times D}$, the model produces a representation of the input data that is
suitable for input to the classifier. The use of sequence pooling in CCT
allows the network to associate data from throughout the input information and
evaluate the sequential embeddings of the latent space created by the
transformer encoder. This allows the model to capture important information
from the entire input sequence, rather than just individual patches, which can
improve its performance on certain tasks. Overall, CCT is a model that
incorporates a convolutional tokenizer, sequence pooling, and a transformer
encoder. This combination of techniques allows CCT to effectively classify
input data by capturing spatial relationships and important information from
the entire input sequence.

##  IV Experimental Results and Analysis

###  IV-A Dataset Preperation

For the purpose of training the ViT model, this experiment makes use of a
sleepiness estimate benchmark dataset called COVID-19 Radiography Database.
This database is one of the most consid- erable datasets accessible among the
public datasets that are currently available. The COVID-19 Radiography
Training Database has a total of 36 participants, each with their own unique
scenario. We gather random photos of each participant and identify them using
a binary system according to whether or not they have covid. This dataset
contains frames with a resolution of 640 by 480, which is considered to be
high in comparison to the resolution of other COVID19 datasets. Significant
adjustments have been made to the size, position, and impressions of the
chests included in the dataset. As a result, this benchmark dataset is
suitable for demonstrating the efficacy and performance in real-world
contexts.

###  IV-B Experimental Setup and Computational Specification

This section covers the hardware and software computing requirements for the
proposed system. The framework was developed using the Tensorflow 2.0, and
OpenCV libraries and was implemented in Python 3.9. The minimum requirements
for the proposed system are listed in the table.

TABLE I: Minimum Requirement for the Proposed System System Specifications | Configuration Details
---|---
Operating System | Windows 11
Random Access Memory | 32GB
System Type | 64 bit Windows OS
CPU | Intel core i9
GPU | GeForce 3070 Ti
Clock Speed | 4.80Ghz
Python | 3.10.4
Frameworks | TensorFlow, Keras

Figure 5: Learning Curves of Training and Validation accuracy and Loss for 150
Epochs and its Confusion Matrix

###  IV-C Distribution of Data

The Distribution of the colour values in the images are taken into
consideration based on the target class to extract useful insights. Insights
for pixels from the plot of Mean vs Density:

  * •

Covid Negative situations have a maximum pixel value that is more than 0.014
but less than 0.016.

  * •

Greater than 0.004 but less than 0.006 is the maximum value for a pixel in
circumstances when Covid is positive.

The Max versus Density plot reveals the following observations about pixels:

  * •

For Covid Negative situations, the maximum pixel value must be larger than
0.035 and less than 0.040.

  * •

The maximum pixel value for situations that are considered to be Covid
Positive is 0.005.

The Minimum versus Density graphic reveals the following about pixels:

  * •

The maximum pixel value for situations that are classified as Covid Negative
is more than 0.4.

  * •

In circumstances when Covid is positive, the maximum pixel value must be more
than 0.0 and less than 0.1.

###  IV-D Evaluating Compact Convolutional Vision Transformers

For the goal of conducting an analysis of the effectiveness of our ViT system,
conventional benchmarks for judging classification models have been used. The
learning curves of accuracy and loss that were experienced throughout the
training and validation of the models are shown in the figure below. Because
both the validation curve and the training curve maintain a point of stability
with a minimal variation between them, these learning plots provide indicative
of an efficient learning method. The training of the efficient ViT model was
developed to incorporate three distinct but interrelated tasks at the same
time. These tasks are as follows: 1) the calculation of output; 2) the
correction of mistakes, and 3) the fine-tuning of the hyper-parameters. The
purpose of this design was to achieve the best possible results from the
training of the model. When employing a certain combination of hyper-
parameters, the maximum training and validation accuracy, respectively, were
determined to be 97% when using a particular combination of hyper-parameters.
This was discovered after a number of rounds during which the hyper-parameters
were fine-tuned.

In order to conduct further assessments of the efficacy of the classification
ViT model, calculations of hamming loss and binary cross-entropy are carried
out. It has been determined that the cross-entropy loss and the hamming loss
for the trained ViT model both equal 0.6907 and that the corresponding hamming
loss and cross-entropy losses are 0.0673. The fact that the log loss was
coming closer and closer to zero was a sign that beneficial results were being
achieved. Cross entropy loss had disproportionately penalized erroneous
predictions, which is a factor that is significant for a loss function but
undesirable for a metric. This is because this factor is a factor that is
important for a loss function but bad for a metric. Cross entropy loss was the
method that was used. As a result, scores for precision, recall, and F1, in
addition to those for several other forms of accuracy, were calculated and
shown.

TABLE II: Calculated Evaluation metrics

| Precision | Recall | F1 Score | Image Support
---|---|---|---|---
Healthy | 0.97 | 0.98 | 0.97 | 844
Diseased | 0.98 | 0.97 | 0.95 | 261
Macro Average | 0.93 | 0.94 | 0.93 | 1105
Weighted Average | 0.95 | 0.95 | 0.95 | “05

###  IV-E Grad-CAM: Gradient-weighted Class Activation Mapping

Gradient-weighted Class Activation Mapping (Grad-CAM) is a technique used in
deep learning to generate a heatmap highlighting the regions in an image that
are most important for a specific prediction. This can be useful for
understanding how a model is making a particular decision, and can also help
with debugging and improving the model.

Figure 6: Activation Maps of the Sample Predictions

The following are the insights obtained by the XAI :

  * •

Positive-1: In its Grad-CAM picture, on the right midsection of it we can see
the blue colour highlighted piece, which is opacity owing to which it belongs
to COVID’s Positive Category. Because of this, it is a sample that has been
given the Positive-1 designation.

  * •

Positive-2: In its Grad-CAM picture, on the left bottom half of it we can see
the blue green hue highlighted region which is consolidation. Because there is
no Tree-Bud owing to this, it is classified as a member of the COVID -
Positive Category.

Negative-1: In its Grad-CAM picture, we are able to view the blue colour
highlighted section that is between the Cardiac and the Diaphragm. Since there
was no opacity identified, it falls into the COVID - Negative Category because
of this.

  * •

Negative-2: Within its Grad-CAM picture, we are able to view the blue colour
part that emphasizes the Trachea. As a result of the absence of any other
opacity, it is classified as falling within the COVID - Negative Category.

##  V Conclusion

We have developed Compact Convolutional Transformers for lung disease
classification in this work. Apart from this we have developed an end-to-end
framework which consists of three phases for classification, phase 1 consists
of image preprocessing using contrast limited adaptive histogram equalization
(CLAHE) & ben graham method, phase 2 includes image augmentation and finally
phase 3 consists of the compact convolution transformer for classification.
CLAHE is operated on small regions of an image and is used to improve the
visibility level of a foggy image and ben graham method is used to eliminate
the color that is considered to be the local average. And in the phase 2 we
have used different data augmentation techniques such as rotation, zooming,
flipping, etc. And finally the CCT model is trained using the training dataset
and it has obtained 97% and 94.6% as training and validation accuracy
respectively. Explainable AI has been implemented in order to comprehend the
reasoning behind a specific decision or action, or to provide an understanding
of how the AI system operates. GradCAM is a technique used to visualize the
region of an input that is used to predict the lesion with the ViT model.

## References

  * [1] Mona Hellou, Fulvia Mazzaferri, Eleonora Cremonini, Elisa Gentilotti, Pasquale De Nardo, Itamar Poran, Mariska Leeflang, Evelina Tacconelli, Mical Paul, and Anna Górska.  Systematic review nucleic acid amplification tests on respiratory samples for the diagnosis of coronavirus infections: a systematic review and meta- analysis.  Clinical Microbiology and Infection, 27, 11 2020.
  * [2] Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Yi Ding, Aarti Bagul, Curtis P. Langlotz, Katie S. Shpanskaya, Matthew P. Lungren, and Andrew Y. Ng.  Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning.  CoRR, abs/1711.05225, 2017.
  * [3] Zhao Wang, Quande Liu, and Qi Dou.  Contrastive cross-site learning with redesigned net for COVID-19 CT classification.  IEEE Journal of Biomedical and Health Informatics, 24(10):2806–2813, oct 2020.
  * [4] M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder, and Priya Podder.  Data analytics for novel coronavirus disease.  Informatics in Medicine Unlocked, 20:100374, 2020.
  * [5] Arata Saraiva, N. Ferreira, Luciano Sousa, Nator Carvalho da Costa, José Sousa, D. Santos, Antonio Valente, and Salviano Soares.  Classiﬁcation of images of childhood pneumonia using convolutional neural networks.  pages 112–119, 01 2019.
  * [6] B. Van Ginneken, B.M. Ter Haar Romeny, and M.A. Viergever.  Computer-aided diagnosis in chest radiography: a survey.  IEEE Transactions on Medical Imaging, 20(12):1228–1241, 2001.
  * [7] Bingzhi Chen, Jinxing Li, Xiaobao Guo, and Guangming Lu.  Dualchexnet: dual asymmetric feature learning for thoracic disease classification in chest x-rays.  Biomedical Signal Processing and Control, 53:101554, 2019.
  * [8] Zohreh Mousavi, Nahal Shahini, Sobhan Sheykhivand, Sina Mojtahedi, and Afrooz Arshadi.  Covid-19 detection using chest x-ray images based on a developed deep neural network.  SLAS Technology, 27(1):63–75, 2022.
  * [9] Linda Wang, Zhong Lin, and Alexander Wong.  Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images.  Scientific Reports, 10, 11 2020.
  * [10] Abdullah-Al Nahid, Niloy Sikder, Anupam Kumar Bairagi, Md. Abdur Razzaque, Mehedi Masud, Abbas Z. Kouzani, and M. A. Parvez Mahmud.  A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network.  Sensors, 20(12), 2020.
  * [11] Hai Nguyen, Hoang Huynh, Toan Tran, and Hiep Huynh.  Explanation of the convolutional neural network classifying chest x-ray images supporting pneumonia diagnosis.  EAI Endorsed Transactions on Context-aware Systems and Applications, 7:165349, 07 2018.
  * [12] Vikash Chouhan, Sanjay Kumar Singh, Aditya Khamparia, Deepak Gupta, Prayag Tiwari, Catarina Moreira, Robertas Damaševičius, and Victor Hugo C. de Albuquerque.  A novel transfer learning based approach for pneumonia detection in chest x-ray images.  Applied Sciences, 10(2), 2020.
  * [13] Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Muhammad A. Kadir, and Saad Kashem.  Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest x-ray.  Applied Sciences, (9):3233, may 2020.
  * [14] Tanushree Roy, Neeraj Sirohi, and Arti Patle.  Classification of lung image and nodule detection using fuzzy inference system.  International Conference on Computing, Communication and Automation, ICCCA 2015, pages 1204–1207, 07 2015.
  * [15] P.B. Sangamithraa and S. Govindaraju.  Lung tumour detection and classification using ek-mean clustering.  In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pages 2201–2206, 2016.
  * [16] Prathistith Raj Medi, Ghanta Sai Krishna, Praneeth Nemani, Satyanarayana Vollala, and Santosh Kumar.  A novel end-to-end framework for occluded pixel reconstruction with spatio-temporal features for improved person re-identification.  In 2023 8th International Conference on Business and Industrial Research (ICBIR), pages 865–870. IEEE, 2023.
  * [17] Ghanta Sai Krishna, Dyavat Sumith, and Garika Akshay.  Epersist: A two-wheeled self balancing robot using pid controller and deep reinforcement learning.  In 2022 22nd International Conference on Control, Automation and Systems (ICCAS), pages 1488–1492. IEEE, 2022.
  * [18] Ghanta Sai Krishna, Kundrapu Supriya, Meetiksha Sorgile, et al.  Lesionaid: Vision transformers-based skin lesion generation and classification.  arXiv preprint arXiv:2302.01104, 2023.