# A Novel Framework for Brain Tumor Detection Based on Convolutional
Variational Generative Models

Wessam M. Salama
Pharos University
<EMAIL_ADDRESS>Ahmed Shokry
American University in Cairo
<EMAIL_ADDRESS>
###### Abstract

Brain tumor detection can make the difference between life and death.
Recently, deep learning-based brain tumor detection techniques have gained
attention due to their higher performance. However, obtaining the expected
performance of such deep learning-based systems requires large amounts of
classified images to train the deep models. Obtaining such data is usually
boring, time-consuming, and can easily be exposed to human mistakes which
hinder the utilization of such deep learning approaches.

This paper introduces a novel framework for brain tumor detection and
classification. The basic idea is to generate a large synthetic MRI images
dataset that reflects the typical pattern of the brain MRI images from a small
class-unbalanced collected dataset. The resulted dataset is then used for
training a deep model for detection and classification. Specifically, we
employ two types of deep models. The first model is a generative model to
capture the distribution of the important features in a set of small class-
unbalanced brain MRI images. Then by using this distribution, the generative
model can synthesize any number of brain MRI images for each class. Hence, the
system can automatically convert a small unbalanced dataset to a larger
balanced one. The second model is the classifier that is trained using the
large balanced dataset to detect brain tumors in MRI images. The proposed
framework acquires an overall detection accuracy of 96.88% which highlights
the promise of the proposed framework as an accurate low-overhead brain tumor
detection system.

Index terms— Brain tumor Computer aided diagnosis (CAD) Convolutional neural
network Transfer learning Variational Autoencoders.

##  1 Introduction

Over the years, cancer by its unstable nature remains a curse to humankind
[26]. Computer-aided diagnosis (CAD) applications are used to assist
neurologists. Brain tumor detection, classification and grading are presented
in [24, 9, 19, 22]. These applications rely on magnetic resonance imaging
(MRI) images of the brain, which are better than computed tomography (CT)
images because they can provide greater contrast to the soft tissues in the
brain compared to CT images. In CAD systems, machine-learning techniques are
widely used to detect and classify brain tumors. The basic step for these
systems is the feature extraction step where the system learns the important
features in MRI images. To this end, several methods of extraction of
monuments have been proposed [29]. The extracted features are then fed into a
structured form model to detect and classify brain tumors. However, to make
this problem computationally tractable these systems usually assume that the
important features in MRI images are independent, which limits their ability
to capture the relationship associated with the nature between the features,
which in turn reduces their accuracy [30].

To address these limitations, several brain-based MRI detection systems have
adopted deep learning. The convolutional neural network models (CNN) and the
transfer learning models are commonly used as solutions for detection and
classification problems. These solutions usually contain two phases. The first
is an offline phase where the deep model is trained using a set of manually
classified MRI images (training data). The second is an online phase that
takes a brain MRI image and determines whether it contains a tumor or not.

The limitations of existing solutions are summarized as follows. The
performance of modern methods is not sufficient given the medical importance
of detection and classification problems. Previous solutions rely on manually
defined tumor areas, before classification. This eliminates the disclosure
problem and makes the classification problem easier. However, it prevents
these systems from being fully automated. On the other hand, automated
solutions developed using CNN and its variants have not been able to
significantly improve performance. This is because CNN and deep learning
models, in general, are data-hungry [27]; i.e. In order to achieve the
expected good performance, it requires large amounts of training data
(classified images). This comes with additional cost due to the need to
perform the assembly and the classification of the brain MRI images. Although
pre-trained models (i.e. transfer learning solutions) can be used to solve the
need for big training data, the performance of such solutions depend on the
pre-training model. If your task (i.e. tumors detection or classification) and
the task that the pre-trained model is trained for are too dissimilar, the
accuracy will be poor using transfer learning [28]. Finally, existing
solutions using either CNN or transfer learning are trained using class-
unbalanced datasets (with respect to the number of training samples for each
class), lowering their detection and classification accuracy [16, 13].

This paper provides a novel general framework for discovering and classifying
brain tumors. The proposed framework can be seamlessly integrated with any of
the existing MRI-based detection and classification systems by processing
their small unbalanced training dataset to produce a larger balanced dataset
which is suitable for training deep learning models. In particular, we employ
two types of deep models. The first model is an innovative deep model to
capture the distribution of the important features in a group of a small
class-unbalanced images dataset. Then by using the distribution of the
important features, the system can generate any number of brain MRI images for
each class. Thus, the system can automatically convert a small unbalanced
dataset into a larger balanced one. The second model is the classifier that is
trained using the large class-balanced dataset to detect brain tumors in MRI
images. Note that while we use two models for generating new samples and for
detecting tumors, our work is different from the previous work where two
models are usually employed for feature extraction task and for classification
[12].

The generator model can provide a dense class-balanced dataset that is
required for training the classifier model. This makes it has a large impact
on many real-world applications that require dense class-balanced training
data. Examples include brain tumor detection, classification, and grading.
However, as the classifier model is trained using the newly generated data
from the generator model, the accuracy of the classification (i.e. tumor
detection) is affected by the accuracy of the generator model. This limitation
can be solved if we carefully train the generator model until it reaches
certain good accuracy.

The proposed framework acquires a comprehensive detection accuracy of 96.88%,
outperforming the most recent detection methods, which highlights the promise
of the proposed framework as an accurate system for brain tumor detection.

The rest of the paper is organized as follows: Section 2 presents our related
work. The proposed framework and the details of the two models are described
in Section 3. The dataset description and the evaluation of the proposed
framework are explained in Section 4. Finally, section 5 concludes the paper.

Figure 1: The proposed framework.

##  2 Related work

###  2.1 Deep learning solutions

Deep learning solutions can learn to generate a high-level feature directly
from raw MRI images. Convolutional Neural Networks (CNN) is a commonly used
deep model in these systems [21]. It can automatically learn the
representation of complex features directly from the data itself. CNN-based
brain tumor detection systems have usually two-stage. An offline phase where a
deep CNN model is trained using a set of classified MRI images (training
data). An online phase that takes a brain MRI image and determines whether it
contains tumors or not. CNN-based systems have been successfully applied to
the problem of detection and classification of brain tumors. In addition, with
the support of parallel GPUs, these technologies have gained tremendous
success. In [12], authors further used two different models for developing an
accurate brain tumor classification. The first is a hybrid model for brain
tumor feature extraction. The second is a regularized extreme learning machine
(RELM) for developing an accurate brain tumor classification.

On the other hand, CNN and its variants have not been able to significantly
improve performance. This is because CNN and deep learning models, in general,
are data-hungry [27]; i.e. In order to achieve the expected good performance,
they require large amounts of training data (classified images).

###  2.2 Transfer learning solutions

Recently, a special class of deep learning, known as transfer learning, has
demonstrated its potential for detecting brain tumors based on MRI [33, 15].
Transfer learning allows the use of a previously trained CNN template, which
has already been developed for another related application. Several techniques
are underway to extract deep features from MRI images of the brain using pre-
trained networks. These techniques demonstrate the transfer learning ability
to work with smaller datasets [20, 2]. Examples include [33] where AlexNet and
GoogLeNet are employed on grading of glioma from MRI images. In [15] a pre-
trained VGG-16 network is used for the diagnosis of Alzheimer’s disease from
MRI images.

On the other hand, the performance of transfer learning solutions depend on
the pre-training model. If your task (i.e. tumors detection or classification)
and the task that the pre-trained model is trained for are too dissimilar, the
accuracy will be poor using transfer learning [28].

###  2.3 Deep learning-based segmentation

Recently, there has been much work in image segmentation [35, 34, 14, 23, 3,
6, 11, 4, 5]. In [35, 34] authors propose a novel cross-modality deep-learning
based framework to segment brain tumors from the multi-modality MRI data. In
[14], authors employ nnU-net for brain tumor segmentation. In [23], authors
proposed an automated segmentation of brain tumors from 3D MRI images where
they used autoencoders to solve the problem of the small training dataset.
Authors of [3] also provide an efficient secure algorithm for 3D image
segmentation. They proposed an algorithm for segmenting medical volumes based
on multiresolution analysis where different 3D volume reconstructed versions
have been considered to come up with a robust and accurate segmentation
results [5].

However, these techniques handle 3D images to perform image segmentation which
makes them relatively computationally expensive compared to the use of 2D
images which is a better fit for brain tumors detection problem [1, 6, 4].

##  3 The proposed framework

Figure 1 shows the proposed framework which can be integrated with any of the
current deep learning-based brain tumor detection systems111Without loss of
generality, we focus in this paper on the brain tumor detection problem.
However, our proposed framework can be applied to other problems such as MRI-
based classification and grading.. The framework takes the brain MRI images
data collected by the traditional MRI-based systems as input, typically class-
unbalanced and small in size. The input brain MRI images are then passed
through the preprocessor module which resizes and normalizes the input brain
MRI images. The framework contains two deep models: the Generator model
$G(x,\theta_{g})$ and the Classifier model $C(x,\theta_{c})$ where $x$ is the
input to each model, $\theta_{g}$ is the generator network parameters and
$\theta_{c}$ is the classifier network parameters. The Generator learns the
distribution of the important features in the preprocessed images. Then, given
the distribution of the important features, the Generator can convert the
small unbalanced preprocessed images dataset to a larger balanced one.
Finally, the large balanced dataset is used to train another deep model, the
classifier, which is used for detection and/or classification. In the balance
of this section, we start by describing the input data format followed by the
details of the Preprocessor and the Generator modules. The latter is the core
contribution of this paper. Finally, we describe the classifier that is used
to detect tumors in MRI images.

###  3.1 Input data

The input to the system is a set of brain MRI images. This set is small in
size and imbalanced with respect to the number of training images per class.
Almost all MRI scanners output images in the standardized medical format.
These images are stored as two-dimensional (2D) grayscale images. Each entry
in the grayscale image stores a value from 0 to 255. This range presents the
trade-off between the efficiency of storing information about the image (256
values fit perfectly in 1 byte) and the sensitivity of the human eye (humans
distinguish a limited number of shades of the same color). The grayscale
images are then passed through the preprocessor module.

###  3.2 Preprocessor

The goal of this module is to resize and normalize the input MRI images.
Firstly, all the input images should be in a fixed size, the grayscale input
images are resized to be images of size 256 x 256 pixels. This allows these
images to be fed into deep models with fixed input size. Secondly, normalizing
the input data generally speeds up learning and leads to faster convergence.
To this end, the grayscale input images are normalized to be in the range of
$[0,1]$. The normalization is in intensity values. A minimum-maximum
normalization technique is followed to scale the intensity values between 0
and 1 [25]. The preprocessed images are then passed through the generator
network.

Figure 2: The generative network structure. Input image of size 256 x 256 is
passed through the encoder network $Q_{\phi}(z|x)$ which has two convolutional
and maxpooling layers to extract the most important features from the input
image $x$. The output from the encoder is transformed to one vector (i.e.
flattened) then passed through a fully connected layer (FC) to get the mean
$\mu$ and the standard deviation $\Sigma$ of the encoder distribution
$Q_{\phi}(z|x)$. We then sample $z$ from the $Q_{\phi}(z|x)$ and pass it
through the decoder network $P_{\theta}(x|z)$ with two deconvolutional and
upsampling (i.e. nearest neighbors) layers to reconstruct the image $\hat{x}$.
We force the generative network to reconstruct the input images while making
$z$ follows the normal distribution.

###  3.3 Generator model

The goal of the generative model is to synthesize new brain MRI image samples
for each class. The generative model is trained using the preprocessed images
to generate new image samples that reflect the typical patterns in the
preprocessed images. Our generative model is a modified version of the
variational autoencoder [18]. In general, autoencoders are a specific type of
feedforward neural networks where the input is the same as the output [7].
They are used to learn key representational information (features) for a
dataset within a low-dimensional latent space in an unsupervised mannar where
they compress the input image into a lower-dimensional latent-space
representation $z$ (embeddings) and then reconstruct the output image from
this representation. Hence, the latent space learns to capture the most
essential information required for reconstruction. However, the latent space
embeddings may be sparsely distributed that makes the key information to be
spread across several clusters in the latent space. Meanwhile, the empty space
between clusters does not capture any useful information which makes sampling
from it creates meaningless results.

To solve this problem, we uses the variational autoencoder (VAE) where a new
constraint is added that the latent space embeddings need to follow certain
predefined distribution $p(z)$ [18]. This distribution is usually selected as
normal distribution [10]. Now, by forcing the latent space embeddings to
follow the normal distribution, the network is forced to fully utilize the
latent space so that information is distributed in a way that allows us to
sample from any point in the latent space to generate new images that reflect
the typical patterns in the original small brain MRI images dataset.
Therefore, we depend on VAE to generate new brain images.

Because brain tumors do not always appear with the same number, same shape and
in the exact same position in the brain MRI images, convolutional and
deconvolutional layers are applied in the implementation of the encoder and
the decoder instead of the regular feedforward layers. These layers can
utilize sliding filter maps that can recognize the tumors’ local patterns
independently of their number, shapes, and positions in the brain MRI images.
Hence, the generator model learns to generate new MRI images with different
numbers of tumors, with different shapes, and in different positions in the
images. Figure 2 shows our convolutional variational autoencoder (CVAE)
architecture. The CVAE network has two main components: the encoder (conv) and
the decoder (deconv). The encoder network consists of several convolutional
layers followed by a fully connected layer and the decoder network consists of
a fully connected layer followed by convolutional layers. The encoder
compresses the brain MRI image input $x\in X$ to get the hidden (latent)
representation $z$ and network parameters $\phi$ as an output. The latent
space $z$ is typically referred to as a bottleneck because the encoder must
learn an efficient compression of the brain image data into a lower-
dimensional space. We refer to the encoder as $Q_{\phi}(z|x)$. We can sample
from this distribution to get noisy values of the representations $z$. The
decoder takes the latent representation $z$ as input and produces the
parameters to the probability distribution of the data and has weights and
biases $\theta$. The decoder is denoted by $P_{\theta}(x|z)$. The loss
function is the negative log-likelihood function defined as follows,

$L(\phi,\theta)=\mathcal{L}_{1}+\mathcal{L}_{2}$ (1)
$\mathcal{L}_{1}=-E_{z\sim Q_{\phi}(z|x)}[\log P_{\theta}(x|z)]$ (2)
$\mathcal{L}_{2}=KL(Q_{\phi}(z|x)||p(z))$ (3)

The loss consists of two terms. The first term is the reconstruction loss. It
is the expected negative log-likelihood of the data. This term forces the
decoder to learn to reconstruct the data which prevents the VAE from
generating meaningless image samples. The second term is the regularizer. It
is the Kullback-Leibler [17] divergence between the encoder’s distribution
$Q_{\phi}(z|x_{i})$ and the predefined distribution $p(z)$. Assuming that
$p(z)=N(0,1)$, the regularizer forces the latent representations $z$ to follow
the standard normal distribution. We employ the stochastic gradient descent to
optimize the loss with respect to CVAE network parameters $\phi$ and $\theta$.

We employ convolutional variational autoencoder (CVAE) model for each class in
the dataset to learn the joint distribution $P(X_{i})$ of input features over
the small training images $X_{i}$ for class $i$222Note that in our detection
problem we have two classes: images have tumors or not. Hence, we used two
identical CVAE models. One is trained with ”Yes” images and another one is
trained with ”No” samples.. After finishing the training phase, the network
can generate new brain MRI images by sampling the latent variables $z\sim
N(0,1)$, then decode $z$ to get new brain image samples $\hat{x}$ from class
$i$. So, we can convert a small unbalanced dataset to a larger balanced one.

###  3.4 Classifier model

This section describes our classifier model and how we increase the model
robustness.

Figure 3: The classifier structure. Input image of size 256 x 256 is fed into
the classifier. The convolutional layer uses 32 filter to generate the
features map. The image is then passed through the maxpooling layer which
downsample image dimentions by a factor of 2. This process is repeated by the
second convolutional and pooling layers. After that, we flatten the output of
the second pooling layer to transform the output to one vector. Finally, a
fully connected layer followed by a softmax layer are used to get the classes
probability distribution.

####  3.4.1 Basic model

Our classifier structure is represented in Figure 3. The classifier model is
assumed to be a binary classifier that detects whether a brain MRI image has a
tumor or not. However, this model can be replaced with any other classifier.
The classifier model is a convolutional neural network (CNN). The input to the
CNN is a brain MRI image. The output is the probability distribution for the
different classes. The classifier consists of three components. The first one
is the convolutional layers which extract the most important features from the
brain MRI image. The second is the pooling layers that downsample each feature
to reduce its dimensionality and focus on the most important elements.
Finally, the fully connected layers are used after flattening the features
(that identified in the previous layers into a vector) to predict the
probability that the brain MRI image has tumors or not.

####  3.4.2 Increasing Model Robustness

To further increase the model robustness, the proposed system employs the
drop-out regularization technique during training [31]. The idea is to
randomly drop neurons from the network during training. The temporarily
removed neurons no longer contribute to the activation of downstream neurons
in the forward pass. Similarly, the weight update process is not applied to
them in the backward pass. This prevents the network from overfitting the
training data.

(a) Iteration 100.

(b) Iteration 300.

(c) Iteration 1700.

(d) Iteration 2000.

Figure 4: Synthetic brain MRI images. Initially at iteration 100 from the
training of generator network, the generator produces vague brain images. As
more we increase training iterations, as more we get clearer brain images that
reflect the typical patterns in the brain MRI images.

(a) CNN classifier.

(b) Transfer learning classifier.

(c) Proposed framework.

Figure 5: Normalized confusion matrix for different approaches.

###  3.5 Discussion

The proposed framework consists of two different models for two different
functions. The generator model target is to generate more data samples for
each class in a small class-unbalanced data. On the other hand, the classifier
model target is used only for classification. The generator model needs to be
trained first to generate the synthetic samples. The original samples and the
synthetic ones are then used for training the classifier. Hence, the two
networks cannot be trained in an end-to-end manner.

The generator network can be employed independently from the classifier
network. It can be used to generate more data samples for any image-based
machine learning problem that needs a dense balanced dataset for training. The
classifier, on the other hand, is used for any image-based classification.
Hence, our framework is general for different detection and classification
problems as we can replace the classifier of the brain tumor detection with
another one that handles another problem (e.g. grading the tumors).

##  4 Experiments and results

This section starts with dataset description, followed by the explanation of
the performance metrics. Finally, we compare the performance of the proposed
framework with other systems. We implement the generator and the
classification networks using Google Colab.

###  4.1 Dataset description

We depend in our experiments on a small dataset that contains a few number of
samples, typically 253 samples. The dataset is openly available in [8] and is
commonly used for evaluating classification in [32]. Each sample in the
dataset has a binary label that indicates whether the brain MRI image sample
has a tumor or not. We used 70% of the samples for training and the remaining
30% for testing.

###  4.2 Framework settings

The performance of the brain tumor classification framework depends on a
combination of generator model parameters and classifier model parameters.
There are three distinct framework settings.

####  4.2.1 Deep CNN model as a stand-alone system (CNN)

The deep convolutional neural network is used as a classification model. The
network is trained using the small training dataset (after pre-processing).
The network architecture is described in section 3.4. The hyperparameters of
the network are heuristically adjusted. RMSprop optimizer is chosen as an
optimizer. A high value for the learning rate makes the optimizer overshoots
the minimum value for the loss, while a small value for the learning rate
increases the training time. We choose the learning rate to be 0.0001. The
loss function is categorical cross-entropy which measures the closeness of the
predicted and actual distributions. The dropout rate is 0.5. Table 1
summarizes the classifier parameters.

####  4.2.2 Transfer learning

A pre-trained modified VGG network followed by a dense layer with 256 neurons
and the output layer with two neurons is trained using the small training
dataset (after pre-processing). RMSprop is used as an optimizer with a
learning rate of 0.0004. The loss function is categorical cross-entropy. The
dropout rate is chosen to be 0.5.

####  4.2.3 Proposed framework

Our proposed framework with two networks: the generator network and the
classifier. The preprocessed images are used to train the generator network
described in section 3.3. Figure 4 shows a sample of generated images using
the generative network. In the start of training the generative network, the
network was not able to generate clear brain MRI images. With increasing the
training time (i.e.number of training iterations), the network generates
clearer images that eventually reflect the typical pattern of the brain MRI
images. We used the generator network to convert the original small training
dataset to a larger one with 1000 samples (500 for each class). The newly
generated large dataset is then used for training the classifier network for
detection. The classifier network is described in section 3.4. We tried
different architectures for the generator and the classifier networks. Table 1
contains the generator and the classifier best parameters in terms of the
detection accuracy.

Table 1: Generator and classifier networks parameters

Parameter | Generator |  Classifier
---|---|---
Architecture | Fig2 |  Fig3
Learning rate | 0.0001 |  0.0001
Batch size | 16 |  32
Dropout rate | 0.1 |  0.5
Number of training epochs | 2000 |  100
Size of input layer | 256x256 |  256x256
Size of output layer | 256x256 |  2

###  4.3 Performance metrics

There are several evaluation tools to assess a classifier amongst them, are
the accuracy, the precision, the recall, and the F1 score. Accuracy is the
measure of a correct prediction made by the classifier. It gives the ability
of the performance of the whole classifier. The accuracy is defined as,

$Accuracy=\frac{TP+TN}{TN+FP+FN+TP}$ (4)

Precision, on the other hand, is the ratio of correctly predicted positive
observations to the total predicted positive observations. High precision
relates to the low False Positive Recall (FPR):

$Precision=\frac{TP}{TP+FP}$ (5)

Recall in this context is also referred to as the true positive rate

$Recall=\frac{TP}{TP+FN}$ (6)

The F1-score is the weighted average of precision and recall. It is used as a
statistical measure to rate the performance of the classifier. Therefore, this
score takes both false positives and false negatives into account:

$F_{1}=\frac{2*(Recall*Precision)}{(Recall+Precision)}$ (7)

###  4.4 Model accuracy

Figure 6 shows the loss and detection accuracy for training and testing for
the classifier network with an increasing number of training epochs. Evidence
from figure 6 that the detection accuracy for training and testing increases
together. It also shows that the loss of training and testing decreases
together. This indicates that the classifier network does not over-train
(over-fit) the training data. This is mainly because the newly generated
variational samples help in generalizing the classifier.

(a) Loss of the classifier.

(b) Accuracy of the classifier.

Figure 6: Loss and accuracy.

###  4.5 Effect of number of generated samples

Figure 8 shows the effect of increasing the number of additional generated
training samples on the system accuracy. The figure shows that, as expected,
increasing number of training samples increases the system accuracy due to the
increase of the quality of the learned model. The good news is that the system
performance saturates after about 2000 samples.

###  4.6 Comparison with other systems

Now, we compare our proposed framework with the CNN classifier model and the
transfer learning model both in accuracy and training time.

####  4.6.1 Accuracy comparison

We compare the proposed framework with other approaches such as training the
classifier network directly using the original training data (CNN) and
training the pre-trained VGG network directly using the original training data
(Transfer learning). Figure 5 shows the normalized confusion matrix for the
different approaches.

Table 2: Summary of performance metrics for the different classifiers.

System | Accuracy | Precision | Recall |  F1-score
---|---|---|---|---
CNN | 90.62% | 90.62% | 90.62% |  90.62%
Transfer learning | 93.75% | 93.75% | 93.75% |  93.75%
Proposed framework | 96.88% | 96.88% | 96.88% |  96.88%

Table 2 summarizes the performance metrics such as Accuracy, Precision,
Recall, and F1-score for the different approaches. The results show that the
different approaches can classify the positive samples (i.e. the samples that
have a tumor) as good as the negative samples. The transfer learning technique
is better than the CNN classifier as it depends on a large number of pre-
trained weights. Finally, the table shows that our proposed framework can
detect tumors better than other approaches. This is because of the ability of
the generative model to augment the CNN with newly generated samples which
highlights the promise of the proposed framework as an accurate low-overhead
brain tumor detection system.

Figure 7: Effect of number of generated
samples on the accuracy.

Figure 8: Training time for the different techniques.

####  4.6.2 Time comparison

Figure 8 shows the training time for the three models. The CNN classifier
model training is the fastest as it contains few layers. Unlike the CNN
classifier, the transfer learning classifier contains a relatively large
number of layers to train. Finally, as our proposed network trains the
generative model before training the CNN classifier, our proposed framework is
the slowest. Note that, the training is achieved in an offline stage. Hence,
it doesn’t affect the models’ online running time where the three models are
fast.

##  5 Conclusion

A novel generalized framework for brain tumors detection and classification is
introduced in this paper. The proposed framework used two different deep
models for two different tasks. The first is a convolutional variational
generative model to convert a small class-unbalanced dataset to a large
balanced one. The second is the classifier which is a convolutional model used
to detect tumors in brain MRI images. The proposed framework acquired an equal
best performance of accuracy, precision, recall, and F1-score of 96.88%,
outperforming other recent systems in the literature. This highlights the
promise of our framework as an accurate brain tumor detection system.

## References

  * [1] Nyoman Abiwinanda, Muhammad Hanif, S Tafwida Hesaputra, Astri Handayani, and Tati Rajab Mengko.  Brain tumor classification using convolutional neural network.  In World congress on medical physics and biomedical engineering 2018, pages 183–189. Springer, 2019.
  * [2] Kaoutar B Ahmed, Lawrence O Hall, Dmitry B Goldgof, Renhao Liu, and Robert A Gatenby.  Fine-tuning convolutional deep features for mri based brain tumor classification.  In Medical Imaging 2017: Computer-Aided Diagnosis, volume 10134, page 101342E. International Society for Optics and Photonics, 2017.
  * [3] Shadi Al-Zu’bi, Bilal Hawashin, Ala Mughaid, and Thar Baker.  Efficient 3d medical image segmentation algorithm over a secured multimedia network.  Multimedia Tools and Applications, 80(11):16887–16905, 2021.
  * [4] Shadi AlZu’bi, Sokyna Al-Qatawneh, and Mohammad Alsmirat.  Transferable hmm trained matrices for accelerating statistical segmentation time.  In 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS), pages 172–176. IEEE, 2018.
  * [5] Shadi AlZu’bi, Yaser Jararweh, Hassan Al-Zoubi, Mohammed Elbes, Tarek Kanan, and Brij Gupta.  Multi-orientation geometric medical volumes segmentation using 3d multiresolution analysis.  Multimedia Tools and Applications, 78(17):24223–24248, 2019.
  * [6] Shadi AlZu’bi, Mohammed Shehab, Mahmoud Al-Ayyoub, Yaser Jararweh, and Brij Gupta.  Parallel implementation for 3d medical volume fuzzy segmentation.  Pattern Recognition Letters, 130:312–318, 2020.
  * [7] Pierre Baldi.  Autoencoders, unsupervised learning, and deep architectures.  In Proceedings of ICML workshop on unsupervised and transfer learning, pages 37–49, 2012.
  * [8] Navoneel Chakrabarty.  Dataset for brain tumor mri images.  2019\.
  * [9] S Deepak and PM Ameer.  Brain tumor classification using deep cnn features via transfer learning.  Computers in biology and medicine, 111:103345, 2019.
  * [10] Carl Doersch.  Tutorial on variational autoencoders.  arXiv preprint arXiv:1606.05908, 2016.
  * [11] Mohammed Elbes, Shadi Alzubi, Tarek Kanan, Ala Al-Fuqaha, and Bilal Hawashin.  A survey on particle swarm optimization with emphasis on engineering and network applications.  Evolutionary Intelligence, 12(2):113–129, 2019.
  * [12] Abdu Gumaei, Mohammad Mehedi Hassan, Md Rafiul Hassan, Abdulhameed Alelaiwi, and Giancarlo Fortino.  A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification.  IEEE Access, 7:36266–36273, 2019.
  * [13] Xinjian Guo, Yilong Yin, Cailing Dong, Gongping Yang, and Guangtong Zhou.  On the class imbalance problem.  In 2008 Fourth international conference on natural computation, volume 4, pages 192–201. IEEE, 2008.
  * [14] Fabian Isensee and Klaus H Maier-Hein.  nnu-net for brain tumor segmentation.  In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II, volume 12658, page 118. Springer Nature, 2021.
  * [15] Rachna Jain, Nikita Jain, Akshay Aggarwal, and D Jude Hemanth.  Convolutional neural network based alzheimer’s disease classification from magnetic resonance brain images.  Cognitive Systems Research, 57:147–159, 2019.
  * [16] Nathalie Japkowicz and Shaju Stephen.  The class imbalance problem: A systematic study.  Intelligent data analysis, 6(5):429–449, 2002.
  * [17] James M Joyce.  Kullback-leibler divergence.  International encyclopedia of statistical science, pages 720–722, 2011.
  * [18] Diederik P Kingma and Max Welling.  Auto-encoding variational bayes.  arXiv preprint arXiv:1312.6114, 2013.
  * [19] Sanjeev Kumar, Chetna Dabas, and Sunila Godara.  Classification of brain mri tumor images: A hybrid approach.  Procedia computer science, 122:510–517, 2017.
  * [20] Renhao Liu, Lawrence O Hall, Dmitry B Goldgof, Mu Zhou, Robert A Gatenby, and Kaoutar B Ahmed.  Exploring deep features from brain tumor magnetic resonance images via transfer learning.  In 2016 International Joint Conference on Neural Networks (IJCNN), pages 235–242. IEEE, 2016.
  * [21] Shih-Chung B Lo, Heang-Ping Chan, Jyh-Shyan Lin, Huai Li, Matthew T Freedman, and Seong K Mun.  Artificial convolution neural network for medical image pattern recognition.  Neural networks, 8(7-8):1201–1214, 1995.
  * [22] Geethu Mohan and M Monica Subashini.  Mri based medical image analysis: Survey on brain tumor grade classification.  Biomedical Signal Processing and Control, 39:139–161, 2018.
  * [23] Andriy Myronenko.  3d mri brain tumor segmentation using autoencoder regularization.  In International MICCAI Brainlesion Workshop, pages 311–320. Springer, 2018.
  * [24] Yuehao Pan, Weimin Huang, Zhiping Lin, Wanzheng Zhu, Jiayin Zhou, Jocelyn Wong, and Zhongxiang Ding.  Brain tumor grading based on neural networks and convolutional neural networks.  In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 699–702. IEEE, 2015\.
  * [25] S Patro and Kishore Kumar Sahu.  Normalization: A preprocessing stage.  arXiv preprint arXiv:1503.06462, 2015.
  * [26] Pottumarthi V Prasad.  Magnetic resonance imaging: methods and biologic applications, volume 124.  Springer Science & Business Media, 2006.
  * [27] Hamada Rizk, Ahmed Shokry, and Moustafa Youssef.  Effectiveness of data augmentation in cellular-based localization using deep learning.  arXiv preprint arXiv:1906.08171, 2019.
  * [28] Michael T Rosenstein, Zvika Marx, Leslie Pack Kaelbling, and Thomas G Dietterich.  To transfer or not to transfer.  In NIPS 2005 workshop on transfer learning, volume 898, page 3, 2005\.
  * [29] Muhammad Sajjad, Salman Khan, Khan Muhammad, Wanqing Wu, Amin Ullah, and Sung Wook Baik.  Multi-grade brain tumor classification using deep cnn with extensive data augmentation.  Journal of computational science, 30:174–182, 2019.
  * [30] Ahmed Shokry, Marwan Torki, and Moustafa Youssef.  Deeploc: a ubiquitous accurate and low-overhead outdoor cellular localization system.  In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 339–348. ACM, 2018.
  * [31] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov.  Dropout: a simple way to prevent neural networks from overfitting.  The journal of machine learning research, 15(1):1929–1958, 2014\.
  * [32] Shanthanreddy Thotapally.  Brain cancer detection using mri scans.  2020\.
  * [33] Yang Yang, Lin-Feng Yan, Xin Zhang, Yu Han, Hai-Yan Nan, Yu-Chuan Hu, Bo Hu, Song-Lin Yan, Jin Zhang, Dong-Liang Cheng, et al.  Glioma grading on conventional mr images: a deep learning study with transfer learning.  Frontiers in neuroscience, 12, 2018.
  * [34] Dingwen Zhang, Guohai Huang, Qiang Zhang, Jungong Han, Junwei Han, Yizhou Wang, and Yizhou Yu.  Exploring task structure for brain tumor segmentation from multi-modality mr images.  IEEE Transactions on Image Processing, 29:9032–9043, 2020.
  * [35] Dingwen Zhang, Guohai Huang, Qiang Zhang, Jungong Han, Junwei Han, and Yizhou Yu.  Cross-modality deep feature learning for brain tumor segmentation.  Pattern Recognition, 110:107562, 2021.