Overview of convolutional neural networks architectures for brain tumor segmentation

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4594~4604
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4594-4604  4594
Journal homepage: http://ijece.iaescore.com
Overview of convolutional neural networks architectures for
brain tumor segmentation
Ahmad Al-Shboul1
, Maha Gharibeh2
, Hassan Najadat3
, Mostafa Ali3
, Mwaffaq El-Heis2
1
Department of Computer Science, Faculty of Computer and Information Technology, Jordan University of Science and Technology,
Irbid, Jordan
2
Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology,
Irbid, Jordan
3
Department of Computer Information System, Faculty of Computer and Information Technology, Jordan University of Science and
Technology, Irbid, Jordan
Article Info ABSTRACT
Article history:
Received Jun 1, 2022
Revised Oct 29, 2022
Accepted Nov 6, 2022
Due to the paramount importance of the medical field in the lives of people,
researchers and experts exploited advancements in computer techniques to
solve many diagnostic and analytical medical problems. Brain tumor
diagnosis is one of the most important computational problems that has been
studied and focused on. The brain tumor is determined by segmentation of
brain images using many techniques based on magnetic resonance imaging
(MRI). Brain tumor segmentation methods have been developed since a long
time and are still evolving, but the current trend is to use deep convolutional
neural networks (CNNs) due to its many breakthroughs and unprecedented
results that have been achieved in various applications and their capacity to
learn a hierarchy of progressively complicated characteristics from input
without requiring manual feature extraction. Considering these
unprecedented results, we present this paper as a brief review for main
CNNs architecture types used in brain tumor segmentation. Specifically, we
focus on researcher works that used the well-known brain tumor
segmentation (BraTS) dataset.
Keywords:
Artificial neural networks
Brain tumor segmentation
Convolutional neural networks
Deep learning
Magnetic resonance imaging
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hassan Najadat
Department of Computer Information System, Faculty of Computer and Information Technology, Jordan
University of Science and Technology
Irbid, Jordan
Email: najadat@just.edu.jo
1. INTRODUCTION
Medical imaging analysis has been widely used in medical diagnosis and remediation, such as
diagnoses using computer-assisted methods, management of information from medical record, robotic
medical devices and image-based applications [1]. Images provide a mechanism to unveil internal organs and
discovering several diseases, where many types of imaging technologies are used for various medical
purposes. Brain tumor segmentation is a medical problem that affects people’s lives because of the moral and
material effects it has on society.
The biopsy is considered as a standard mechanism that is used for tumor diagnosis, but it is a
lengthy process and invasive that it may cause bleedings or injuries causing functionality loss for the brain
[2]. Consequently, using non-invasive magnetic resonance imaging (MRI) can be a safer and better tool
specifically if accurate and robust approaches are being used for the segmentation. Many MRI procedures
can be performed such as MRI for showing different organs, MRI that study the organs functions, diffusion-

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Overview of convolutional neural networks architectures for brain tumor segmentation (Ahmad Al-Shboul)
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weighted imaging (DWI) and diffusion tensor imaging (DTI) where every procedure is employed for a
certain specific task. Since the structural MRI visualizes wholesome brain tissues and depicts gross brain
structure, vascularity system, radiation-induced microhaemorrhage and calcification, so it is proper to be used
by brain tumor segmentation methods to identify aberrant from normal tissue. The structural MRI sequences
incorporate T1-w, T2-w, fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-w [3].
Manual brain tumor segmentation problem is a slow process, prone to inter rater variability and
tedious work because for every patient the MRI scan generates a large number of slices that must be
delineated. Also, the different types of artifacts in images result in low quality images that prohibit specialists
from the correct and accurate interpretation and diagnosis. So, researchers developed many methods to
automate the process of brain tumor segmentation like region-based segmentation, supervised machine
learning-based algorithms for brain tumor segmentation and deep learning-based methods for tumor
segmentation [4].
During the past few years, deep learning techniques were the state-of-the-art methods with eminent
results, specifically convolutional neural networks (CNNs). Many surveys have been published regarding
deep learning methods in the medical field and brain tumor segmentation, but we noticed that there is not a
specific study for CNNs based brain tumor segmentation methods. The closest paper to ours was presented
by Bernal et al. [5].
Bernal et al. [5] presented a review that focused on the usage of deep CNNs for brain image
analysis. Their work is an extended survey paper that concentrated on CNN techniques which were utilized
in brain analysis using MRI focusing on their architectures. Dedicated preprocessing steps, data-preparation
and post-processing techniques are also included in their work. As mentioned in [6], a brief is introduced a
bout medical image analysis.
Akkus et al. [7] also presented a detailed survey that mentioned many well-known datasets,
preprocessing steps and the styles of training deep learning architectures for brain tumor segmentation.
Magadza and Viriri [8] also plainly clarified the building blocks of the deep learning methodologies that were
considered as state-of-the-art in the task of segmenting tumors from the brain. This survey focused on the
works that used CNNs variants in the field of brain tumor segmentation along with the datasets used and the
results which were obtained. Magadza and Viriri [8] particularly focused on the best performing applied
methods on BraTS dataset for the years 2017, 2018, and 2019. Section two presents architectural details
about main CNNs components.
2. CONVOLUTIONAL NEURAL NETWORKS
CNNs are special feedforward neural networks specified to process data pixels. This type of
network deals with grid-like data such as time series and images data [9]. The main layer in the CNN
architecture that distinguishes it from other types of artificial neural networks (ANNs) is the persistence of
the convolution layer, hence the name of this type of the network. The general architecture is mainly
composed of three building block layers including convolution layer, pooling layer, and connected layer.
Figure 1 illustrates the general architecture of the CNN network. CNN models increasingly learn the
features within data, such that the lower-level layers begin to learn small local patterns, whereas the higher-
level layers learn larger patterns (shapes) synthesized of features from the previous layers and so forth. This
ability makes them maximal choice for image analysis and different processing tasks than other usual ANNs.
Brain tumor segmentation from MR images can greatly benefit from CNNs [8].
2.1. Convolution layer
In this layer, the image is convolved with many two-dimensional (2D) or sometimes three-
dimensional (3D) filters (kernels), this can be determined according to the input dimensions to make
automatic feature extraction. For example, the filter may have the form of (3×3) or (3×3×3) dimensions.
Since the filter convolution against the images allows weight sharing, it reduces the model complexity.
Filters are spatially small patches (windows) that are moved to every possible position on the input matrix
(image) to extract the specific types of features, so convolutions in CNNs can be looked as feature extractor.
The result of the convolution operation (element-wise multiplication) is a feature map which is fed
to the next layer. Also, one main component of CNNs is the activation function that is used to fire the output
of layer neuron, sometimes called (transfer function), it adds nonlinearity to the network. Rectified linear unit
(ReLU) is a well-known and commonly used activation function which replaces the negative output values to
zero.
Figure 2 illustrates the convolution operation. As noted in Figure 2, the convolution operation has
two parameters: the first is the window size which is the step in which the window moves through the image
being sub-sampled, it is 3×3 in this example and the second parameter is the stride which is the transition
step for the window, it is 1 in this example. In the context of improving the performance of CNNs, many

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enhancements were performed in the literature where conventional convolutional layers were replaced with
blocks that rise the network’s capability. For example, Szegedy et al. [10] introduced the inception block that
aided in capturing the sparse correlation patterns. Another notable improvement was the residual block which
was presented by He et al. [11]. It facilitated the building of very deep networks that overcome the problem
of vanishing gradient. Also squeeze-and-excitation (SE) block was introduced by Hu et al. [12] which
enabled capturing the inter-dependencies between the generated feature maps of the network.
Figure 1. Convolutional neural network architecture
Figure 2. Convolution operation
2.2. Pooling layer
A pooling layer typically follows a convolutional layer or many consecutive existing convolutional
layers in the model. Pooling layers are usually added between two convolution layers. The pooling layer aims
to minify the spatial size dimensionality of feature map representation. Feature map passes through the
pooling layer to generate pooled (compressed) feature map or activation map. Many pooling operations can
be used in the pooling layer, the most common are the max pooling and the average pooling. The maximum
value is returned by max pooling when applying the window filter while the average pooling returns the
average of the values covered by the filter. Max pooling is illustrated in Figure 3.
Figure 3. Max pooling operation

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2.3. Fully connected layer (FC)
After convolution and pooling of the input data, the resultant output must be flattened and fed into a
regular artificial neural network layer (fully connected layer) where every layer neuron is connected with
every neuron in the preceding layer. There may be more than one dense or fully connected layer (FC), but the
last one (output layer) must contain many neurons equal to the number of classes in the data for the
prediction. It computes the class probability scores and determines input data affiliation to which class.
Additionally, different layers are added to prevent the problem of overfitting, such as dropout layers and
normalization layer that keeps the mean close to 0 and the standard deviation close to 1 for the output. This
layer will hence accelerate training [13].
The main problem with using FC layers is the needing for extravagant number of parameters
comparatively to other types of layers, which will decrease the efficiency of the network and increase the
network computational cost. Another problem with using FC layers is the necessity for a unified size for an
input image. As a good solution to this problem, Shelhamer et al. [14] proposed replacing FC layers by 1×1
convolutional layers, this will transform the network to be a fully convolutional network (FCN). By this
modification, the network has the capability to receive arbitrary sizes of the inputs and produces
classification maps.
3. CONVOLUTIONAL NEURAL NETWORKS VARIANTS
Designing effective modules and network architectures have become one of the important factors
for achieving accurate segmentation performance [1]. So different updates in CNNs architecture have been
innovated, these improvements comprise the optimization of parameters, regularizing the network, reforming
network structure. It was obviously noticed that the essential reason for increasing the performance of CNN
comes from restructuring of processing units and the designing of new blocks [15]. So many variants of
CNNs were utilized by researchers for brain tumor segmentation. According to the characteristics of network
structures, this paper divides CNNs for brain tumor segmentation into single/multiple path networks. In the
next subsections, these types will be elaborated with many examples from the literature.
3.1. Single/multiple path networks
Single and multiple path networks are used to extract features and classify the center pixels of the
input patch, which is a part of the image. In single path networks, data stream happens from the input layer to
the classification layer through a single path. Pereira et al. [16] proposed a fully automatic brain tumor
segmentation based on CNN with kernels of 3×3 and used the ReLU as an activation function. The
architecture of their CNN consisted of 11 layers. They used normalization as a preprocessing step and data
augmentation (rotation) in their method, which were effective for brain tumor segmentation in MRI as they
stated in their work. The method was performed using the BraTS dataset for training and validation and
achieved the first position for the complete, core and enhancing regions in the dice similarity coefficient
(DSC) metric with 88%, 83%, and 77% respectively for the challenge 2013 dataset. They also took place in
the on-site BraTS 2015 competition using the same suggested model achieving the rank two with a DSC
metric of 78%, 65%, and 75% for the complete, core and enhancing regions, respectively. The data
comprised four sequences for every patient: T1, T1c, T2 and FLAIR. In comparison to single path networks,
existence of several paths for the networks can elicit various features from these paths with multiple scales. A
large-scale path (path with a large kernel size or input) allows CNN to learn global features, while small scale
paths (paths with a small kernel size or input) allow CNN to learn features known as local features or
descriptors. The usage of bigger sizes of kernels produces global features which tend to supply global
informative view for example: tumor location, size and shape, while local features present more descriptive
details such as tumor texture and boundary.
Zikic et al. [17] investigated deep learning CNN in the segmentation of brain tumor tissues. Their
work was inspired and motivated by the good results achieved by Krizhevsky who used CNNs for object
recognition on 2D images of the LSVRC-2010 ImageNet. For each point to be segmented, they used
information from the surrounding patch. The CNN was trained to make a class prediction for the central
patch point x. They used a standard CNN that contains just 5 layers and stochastic gradient descent with
momentum (SGD) to perform the segmentation on the BraTS dataset of four sequences T1, T2, T1c and
FLAIR. They stated that preliminary results indicate that even the unoptimized CNN architecture is capable
of achieving acceptable segmentation results.
The work of Havaei et al. [18] is one of the early multipath CNNs. They proposed a CNN that was
utilized to exploit simultaneously local features and global contextual features, and uses a fully convolutional
final layer instead of fully connected layer hence decreases network complexity and increases the speed of
training. Two types of architectures were explored in their work. The first is Two-pathway architecture in
which there are two paths, one with 7×7 receptive fields and another with larger 13×13 receptive fields.

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Havaei et al. [18] called these paths as local pathway and global pathway, this allowed the pixel
label classification to be affected by the region around the pixel and also with larger context where the patch
in the brain. The feature maps of both paths then were concatenated to be the input for the final layer for
classification. Tow-pathway architecture achieved a DSC accuracy 85%, 78%, 73% for complete, core,
enhancing tumor regions, respectively with dataset BraTS 2013. The second type of architectures used by
Havaei is Cascaded architectures that aimed to model the direct dependencies between the close labels that
have adjacency. The authors suggested and explored three cascaded architectures versions namely, input
concatenation (InputCascadeCNN), Local pathway concatenation (LocalCascadeCNN), Pre-out
concatenation (MFCascadeCNN). The best version was Input concatenation (InputCascadeCNN) which
achieved DSC accuracy 88%, 79%, 73% for complete tumor region, core tumor region, enhancing tumor
region, respectively.
Rao et al. [19] also used CNN to segment tumors from a large dataset of brain tumor MR images
supplied by BraTS 2015. They used four sequences of T1, T2, T1c and FLAIR. Every sequence was trained
by a CNN architecture and the output of each CNN version was taken as the representation for that sequence.
Then, these representations were concatenated to be the input to a random classifier, which achieved an
accuracy of 67%. Iqbal et al. [20] presented deep learning models utilizing long-short-term memory (LSTM)
and CNN to exact brain tumor delineation (segmentation) from benchmark medical images. LSTM and
ConvNet were trained on the same data and then merged to get an ensemble method for more improvement.
The authors used BraTS 2015 which contains (274 subject data) for four modalities: T1, T1c, T2 and FLAIR.
The authors divided the 3D data into ratios of 60:20:20 for training, evaluation and testing respectively and
converted them to 2D images (slices) then extracted patches of size 25×25. The authors tried to solve the
problem of class imbalance by using some methods such as weight-based balancing. Experiments showed the
usefulness of using LSTM in segmentation. The DSC obtained was 82%, 79% and 77% for complete tumor
region, core tumor region and enhancing tumor region respectively.
Hoseini et al. [21] proposed the so-called AdaptAhead as new optimization algorithm for CNN
learning. It is based on merging of two optimization algorithms: Nesterov and RMSProp. The proposed
model had eight layers and used 3×3 filters. The data was used from BraTS 2015 and BraTS 2016. When
comparing the results of their introduced optimization algorithm against some existing related works for
tumor segmentation from MRI, they found that their algorithm is more accurate about the metric of DSC, as
they obtained 89% and 85% in BraTS 2015 and BraTS 2016, respectively.
Zhao et al. [22] suggested a novelty paradigm for brain tumor segmentation by the integration of
fully convolutional neural networks (FCNNs) with conditional random fields (CRFs) into a single conjoined
framework. FCNNs are used to train data in a 2D patch-wise way and CRF-RNNs are used to train 2D image
slices. Through the integration of them as one network, the model achieved 84%, 73% and 62% for the
complete tumor region, core tumor region and enhancing tumor region, respectively. Experiments were
performed on BraTS 2013 dataset.
Liu et al. [23] presented a novel two-task approach for the segmentation of brainstem tumors and to
make a prediction for the genotype (H3 K27M) mutation status based on 3D magnetic resonance (MR)
images. They proposed and trained a 3D multiscale CNN model with 55 manually labeled patient datasets of
the T1c sequence. Their proposed network consists of two components: the first is a multiscale feature fusion
convolutional network that aims to obtain the tumor mask from input images and the second component is
the H3 K27M-mutation-status-prediction network which is a CNN to extract features from the tumor mask
and then using a SVM classifier to gain high accuracy prediction for the genotype. The experiment results of
their two-task proposed method gave a DSC of 77% in the task of brainstem segmentation and accuracy of
96% in genotype prediction.
Razzak et al. [24] described a Two-PathwayGroup CNN architecture for brain tumor segmentation
where local features and global contextual features were exploited simultaneously. The applied filters
performed and exploited many transformations like translation, rotations and reflections processes.
Experiments were performed on BraTS2015, the results obtained were 89.2%, 79.1%, and 75.1% for
complete tumor region, core tumor region and enhancing tumor region, respectively. Also, Cui et al. [25]
presented a fully automatic segmentation method from MRI data, based on cascaded CNN. The method
aimed to localize the tumor region and then accurately segment the intratumor structure by using two
subnetworks: a tumor localization network (TLN) and an intratumor classification net-work (ITCN). The
TLN subnet was used to localize the brain tumor and then, the ITCN subnet was applied for further
classification of tumor sub-regions. The BraTS 2015 dataset of 274 patients was used for training and testing
their method and four sequences for the images T1, T1c, T2 and FLAIR were used. This method gained DSC
of 90%, 81%, 81% for the complete tumor, core tumor, enhancing tumor regions, respectively.
Naceur et al. [26] suggested end-to-end deep CNN architectures for fully automated brain tumor
segmentation. Their three architectures which follow incremental approach in their building differ from the

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usual CNN-based models which use a trial-and-error technique to find the optimal hyper-parameters. Instead,
a new training strategy was proposed that consider the most influential hyper-parameters where a roof setting
was bounded over these hyper-parameters to speed up the training process. The main concept behind the
incremental deep CNN strategy is to add a new block at the end of each training phase (a block is composed
of several convolutions and pooling layers). So, creating a CNN model to give a high prediction performance
at the same time as designing a network architecture that is optimized in terms of layers. Three models of
CNN were utilized, the results of their models were competitive in terms of DSC metric on the public dataset
BraTS 2017. In terms of DSC metric, the authors obtained 88%, 87% and 89% for the three models that were
used in discovering the whole tumor.
Wang et al. [27] proposed a cascade of many CNNs to perform segmentation with hierarchical sub-
regions from MR images and introduced a 2.5D network that is a trade-off between consumption of memory
and complexity of the model. Three networks (WNet, TNet, and ENet) were used to segment the whole, core
and enhanced tumor core structures, respectively. The pipeline work for this approach consists of three
stages. First, segment the whole tumor from the image, then the input is being cropped with respect to the
bounding box of the segmented whole tumor. Second, the tumor core is segmented by TNet from the cropped
image region, and the image is cropped again with respect to the bounding box of the segmented core region.
Eventually, ENet used to segment the enhancing core from the second cropped image. The proposed method
was validated with 3D BraTS 2017 and BraTS 2018. The average DSC achieved by their method for
enhancing tumor core, whole tumor and tumor core was 78.6%, 90.5% and 83.8%, respectively with BraTS
2017 and the average DSC achieved for desired enhancing, whole and core was 73.4%, 86.4% and 76.6%,
respectively with BraTS 2018.
3.2. Encoder-decoder architecture
This is also one of the most used CNNs variants in brain tumor segmentation. This network usually
divided into a contracting path well-known as (encoder) and an expanding path well-known as (decoder), this
what cause the architecture to be a u-shaped [1], [8]. The contracting path (part) consists of the frequent
implementation of many convolutional layers followed by the activation function ReLU and max-pooling
layer such that a reduction in spacial information is performed and the feature information is enlarged. The
expansive path consists of a sequence of many corresponding up-sampling operations merged with different
features taken from encoder part through the usage of skip connections. Getting a high accuracy of mapping
from the patch level to the category label is difficult because of effect of input patch size and quality. Also,
the mapping is mostly directed by the last fully connected layer. So, FCN and encoder-decoder CNNs solve
and overcome these problems by establishing an end-to-end fashion from the input image to the output
segmentation map.
Kao et al. [28] presented a technique that integrates location information with neural networks by
using the brain parcellation atlas found in the Montreal Neurological Institute (MNI) and mapping this atlas
to the individual subject data. They integrated the atlas with MR image data and used patches to enhance the
brain tumor segmentation. Two different CNN architectures were used, DeepMedic and 3D U-Net. They are
frequently used for image segmentation. They used data from four modalities (T1, T1c, T2, and FLAIR) from
BraTS 2017 and BraTS 2018 datasets with using normalization. To clarify the advantage of their proposed
location fusion strategy, they performed several experiments that showed improvements in brain tumor
segmentation performance. Their measures were DSC and Hausdorff distance. Wang et al. [29] segmented
brain tumor into different regions by using cascaded fully CNNs. They converted the tumor segmentation
process into three sequential binary segmentation stages. First, they segmented whole tumor and then used
the result to segment the tumor core and finally, the enhancing core was segmented from the tumor core
result. First experiment was conducted on BraTS 2017 validation dataset with Dice scores of.78%, 90%, 83%
for enhancing core, whole tumor and tumor core, respectively. The second experiment was conducted on
BraTS 2017 testing dataset with Dice scores of 785%, 90%, 83% for enhancing core, whole tumor and tumor
core, respectively. The corresponding values for BraTS 2017 testing set were 78%, 87% and 77%,
respectively. A modified version of U-Net for segmenting tumors was used by Isensee et al. [30], a dice loss
function was used and substantial data augmentation was performed to restrain overfitting. They achieved
very good DSCs on the testing part of BraTS 2017: 85.8% for whole, 77.5% for core, and 64.7% for
enhancing tumor regions
Sun et al. [31] presented a deep learning-based pipeline for brain tumor segmentation and prediction
of survivability for glioma patients using MRI scans. They used an ensemble of three deep CNN
architectures for tumor segmentation. The first network they used was cascaded anisotropic convolutional
neural network (CA-CNN) which was presented previously by Wang et al. [29]. The second employed
network was DFKZ Net, which was suggested by Isensee et al. [30] of the German Cancer Research Center
(DFKZ). The third network used was the well-known U-Net which is a classical network for segmenting
biomedical images tasks. After they obtained the results of segmentation, they extracted features from

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different tumor sub-regions and used a random forest regression model to predict the survivability. The
BraTS 2018 dataset was used in this work including the modalities T1, T1c, T2 and FLAIR. By using the
ensemble method, the approach achieved an average DSC of 77%, 90%, 85% for enhancing tumor, whole
tumor, core tumor regions, respectively.
Wang et al. [32] presented the nested dilation networks (NDNs) as 3-dimensional multimodal
segmentation method which is a modification of U-Net architecture. To enrich the low-level features,
residual blocks nested with dilations (RnD) were used in the contracting part while SE blocks were used in
both the encoding and decoding paths to boost significant features. SE blocks allow enhancing the features
representations derived by a convolutional network, while RnD can enlarge the receptive fields without
reducing the resolution or increasing the number of parameters. Their method obtained DSC results of
66.5%, 58.8% and 66.8% for edema, non-enhancing and enhancing tumors, respectively.
Li et al. [33] used a modification of the U-Net architecture. They utilized an end-to-end cascaded
pipeline for segmentation task. They used to skip up connections between the encoding path and the
decoding path in order to improve information flow, and an inception module was adopted in each block to
help their network pick up richer information representations. The experiments were conducted on 2D slices
of four sequences: T1, T1c, T2 and FLAIR of BraTS 2015. Their cascaded end-to-end method achieved DSC
performances of 84.5%, 69.8% and 60.0% for the complete tumor region, core tumor region and enhancing
tumor region, respectively.
Jiang et al. [34] participated in segmentation task of BraTS 2019 contest. BraTS consisted of
335 patients as a training set. By using a two-stage cascaded 3D U-Net to segment the substructures of brain
tumors, they were the first-class winners in the challenge among more than 70 teams participated in the
contest. Very good results in the terms of DSC were obtained on the testing data of BraTS 2019, which
comprises 125 patient cases. Intensity normalization and three types of augmentation were performed on the
data during the preprocessing step. The DSC for their method was 88.7%, 83.6%, and 83.2% for the whole,
core and enhancing tumor regions, respectively.
In another work, Kao et al. [35] used a methodology to make integration between the existing brain
parcellation atlas in the MNI152 into each subject in the dataset. The experiments were conducted using
BraTS 2018. Using brain parcellation masks as extra inputs to this neural network which used patches
improved the network in brain tumor segmentation. Using DeepMedic with brain parcellation (BP) gave
76.6%, 89.4%, and 80.4% for enhancing tumor regions, whole tumor regions and core tumor regions,
respectively. Also, using 3D U-Net with BP gave 76,4%, 89.4%, and 77.5% for enhancing tumor region,
whole tumor region, core tumor region, respectively.
Kermi et al. [36] used modifications of the 2D U-net architecture; for example, WCE and GDL were
employed as loss functions to reduce the class imbalance issue in the brain tumor datasets. Experiments were
conducted on both the BraTS 2018 dataset for testing and evaluation. They trained the model on the training
dataset of BraTS 2018 with 285 patients and validation data of 66 patients. The results obtained in terms of
DSC were 78.3%, 86.8%, and 80.5% for enhancing tumor region, whole tumor region and core tumor region.
Tseng et al. [37] presented an architecture of encoder-decoder. They used multi-modal encoder
where every MRI modality were trained by a different CNN. They conducted experiment on BraTS 2015
training dataset where 244 subjects were used for training and testing the model on 30 subjects. A DSC
scores of 85.22%, 68.35%, and 68.77% were achieved. Myronenko [38] proposed a CNN that is encoder-
decoder architecture and added a variational auto-encoder (VAE) as extra branch at the end of encoder to
reconstruct the original image. VAE is added as a regularization for the encoder in the lack of data case and
the model was trained on BraTS 2018 training dataset. The model was tested on BraTS 2018 validation
dataset which is 66 subjects with a DSC scores of 81.45%, 90.42%, 85.96% for enhancing, whole, core
tumors, respectively. Also, it was tested on BraTS 2018 testing dataset which is 191 subjects with a DSC of
76.64%, 88.39%, 81.54% for enhancing, whole, core tumors, respectively.
Peng et al. [39] proposed a 3D multi-scale encoder-decoder that uses several U-Net blocks. These
blocks enable the model to the get spatial information at different resolutions in the encoder part. Also,
feature maps were upsampled at different resolutions where, 3D separable convolutions were used as an
alternative to the ordinary convolutions. They achieved a DSC scores of 85%, 72%, and 61% for the whole,
core and enhancing tumors, respectively on BraTS 2015 dataset.
Hua et al. [40] proposed a cascaded V nets version that has encoder and decoder to segment tumor
in two stages. The same model was used in two stages where first, whole tumor was segmented then, it was
divided into other substructures (edema, core, enhancing). They trained their model on BraTS 2018 training
dataset and tested many other datasets. They achieved a DSC of 87.61%, 79.53%, and 73,64% for edema,
core and enhancing, respectively for the testing set of BraTS 2018. A dice scores of 90,48%, 83.64%, and
77.68% were achieved for the same regions mentioned above for BraTS 2018 validation dataset which is

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68 subjects. Also, they tested the performance of their model on a special dataset of 56 subjects where they
achieved a DSC of 86.35%, 80.36%, and 72.17% for whole, core and enhancing tumor regions, respectively.
Wang et al. [41] used the transformer with 3D CNN for brain tumor segmentation. They used
encoder-decoder model where the encoder extracts the spatial feature maps then, these are fed into the
transformer to model the global context and finally, decoder uses transformer output to get the prediction
map. They trained and tested their model on BraTS 2019 validation dataset where they achieved a DSC of
78.93%, 90%, and 81.94% for edema, whole tumor, core tumor respectively. Also, the DSC results were
78.73%, 90.09%, and 81.73% for edema, whole tumor, core tumor, respectively on BraTS 2020 validation
dataset.
Zhou et al. [42] proposed a model that has different encoders for each MRI modality. Then, the
resultant feature maps were concatenated by a fusion block. Finally, the concatenated feature maps were
passed to the decoder to obtain the final segmentation results. Experiments were performed on BraTS 2017
with a DSC scores of 87.7%, 79.1%, and 73.9% for whole, core, enhancing tumors, respectively.
Khan et al. [43] presented pyramidical encoder-decoder model that has six cascaded levels to extract
the segmentation predictions at different image scales. At each level, encoder-decoder model, predicts the
segmentation maps from the input images. The input images then, doubled and the prediction maps are sub-
sampled to fit the size of the images. Then, predictions and images with new size are concatenated and used
as inputs for next level. They performed experiments on many medical datasets, one of them was the TCIA
brain tumor dataset, where they achieved intersection over union (IoU) of 83.39%.
Rehman et al. [44] proposed the BrainSeg-Net encoder-decoder network which uses a new block
called feature enhancer (FE). The feature maps of each encoder block are passed to the (FE) to extract
middle-level features from the shallow layers and propagate them with the dense layers in the decoder. This
model achieved a DSC scores of 90.3%, 87.2%, 84.9% for whole core, enhancing regions, respectively.
Chen et al. [45] proposed CSU-Net encoder-decoder model that consists of two branches in the
encoder part, a CNN and transformer, and the decoder is based on dual Swin transformer. They achieved a
DSC scores of 81.88%, 88.57%, and 89.27% for enhancing, core, whole tumor regions, respectively on
BraTS 2020 dataset. Zhang et al. [46] proposed multi-scale mesh aggregation network (MSMANet). In the
encoder part, they used modified Res-Inception and SE modules for feature extraction. the decoder was
replaced by aggregation block. BraTS 2018 dataset was used to evaluate their model which achieved a DSC
scores of 75.8%, 89%, 81.1 % for enhancing, whole, core tumors, respectively.
Maji et al. [47] proposed attention Res-UNet with guided decoder (ARU-GD), that is a modified
version of Res-Unet, with attention gates and guided decoder. In this model, each decoder layer was trained
individually and the prediction result was upsampled to the original size of the input image to be compared
with ground truth of the image. Attention gates were used instead of skip connection to pass only the relevant
spatial and contextual features between encoder and decoder. This model was trained on 6,700 images from
BraTS 2019 and achieved a DSC scores of 91.1%, 87.6% and 80.1% for whole, core and enhancing tumors,
respectively.
Shan et al. [48] proposed 3D CNN based on U-net architecture. Their model comprised three main
units: improved depth-wise convolution (IDWC) unit which uses separable convolution instead of
conventional convolution to extract feature maps and computationally saving resources. Multi-channel
convolution (MCC unit), which makes convolution with different kernel sizes, enabling the network to get
features from different receptive fields. SE unit to obtain the final tumor prediction. The model was trained
on training set of BraTS 2019 and tested on validation set of BraTS 2019 with DSC scores of 90.53%,
83.73%, and 78.47% for whole, core, enhancing regions, respectively.
Aghalari et al. [49] proposed a modification on U-net architecture, by the addition of two-pathway
residual blocks (TPR), where this block has two streams: one as local path consists of (3×3) convolutional
layer then residual block to capture local information while the second stream is (5×5) convolutional layer to
capture global information. Experiments were performed on training set of BraTS dataset that contains 285
patients. Data was divided as 70% for training, 15% for validation and 15% for testing. Average DSC of
89.76% was obtained.
Rehman et al. [50] proposed 2D segmentation method (BU-Net) based on U-net model. They added
two blocks: Residual extended skip (RES) and wide context (WC) block to U-net model. The network is still
encoder-decoder model with using RES block to derive middle features from low features and WC block is
used in the transition between contracting and expensive path. They conducted their experiments on BraTS
2017 with DSC scores of 89.2%, 78.3%, 73.6% for whole, core, enhancing tumor regions, respectively and
also on BraTS 2018 with DSC scores of 90.1%, 83.7%, 78.8% for whole, core, enhancing tumor regions,
respectively.
Zhang et al. [51] proposed 2D attention residual U-Net (AResU-Net) for brain tumor segmentation
which is a U-Net based. This model is conventional encoder-decoder that includes three residual blocks in
the encoder path and the decoder path also, includes three upsampling residual blocks. Finally, attention and

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squeeze and excitation block (ASE) was utilized between upsampling and downsampling paths. To evaluate
their system, they performed many experiments on subsets data of BraTS 2017 and 2018. HGG cases from
BraTS 2017 which are 168 patients were divided into training and testing with ratio of 80:20, they achieved
DSC scores of 89.2%, 85.3%, 82,5% for whole, core, enhancing tumors, respectively. Also, they performed
another experiment on BraTS 2018 dataset where they used the training data of 285 subjects and tested the
system on validation set which is 66 subjects with DSC scores of 87.6%, 81%, 77.3%.
4. CONCLUSION AND FUTURE WORK
Deep CNNs have been remarkably developed and many architectures have been utilized in many
applications. Brain tumor segmentation process, is a task regarding the medical field that benefited from
CNNs technology where, several research works are being continuously conducted to improve the efficiency
of CNNs for segmentation. The updated improvements in CNNs can be classified according to different
ways, comprising activation and loss functions, optimization, regularization techniques, the novelties in the
learning algorithms architectures. In this paper, we review CNN variants that were used in brain tumor
segmentation having a focus on the architectural taxonomy of the networks. we noticed from the existing
works that the most used CNN variants are: conventional CNN (either single, multiple or cascaded paths) and
encoder-decoder frameworks. Also, we focused on the works, which used the well-known BraTS dataset
with four modalities (T1, T1C, T2, FLAIR) and considered a DSC metric for result evaluation, as this metric
is widely used in segmentation evaluation tasks. Unlike some reviews, researcher’s results were included in
our overview. In the Future, this survey will be extended to contain most brain tumor segmentation works
that relied on using CNNs. A detailed study of different CNNs variants that explains their architectures
techniques and articulate advantages and disadvantages, listing the datasets and including different
augmentation and prepossessing techniques also is required and would enrich the study to be a
comprehensive reference in this field.
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BIOGRAPHIES OF AUTHORS
Ahmad Al-Shboul received B.Sc. of Computer Science from Yarmouk
University, 2005 and M.Sc. of Computer Science from Jordan University of Science and
Technology, 2022. Currently, he is a Computer Trainer at the Ministry of Digital Economy and
Entrepreneurship, Jordan. His research interests include data mining, artificial intelligence. He
can be contacted at email: aaalshbool16@cit.just.edu.jo.
Maha Gharibeh received MBChB of medicine from JUST in 2009, JMCC in
diagnostic radiology in 2009 and FRCR/part 1 from RCR/London 2010. She is an assistant
Professor at the Department of Diagnostic Radiology and Nuclear Medicine, King Abdullah
University Hospital. Her research interests include computed tomography, diagnostic
radiology, magnetic resonance, interventional ultrasonography, breast cancer, screening
imaging, medical imaging. She can be contacted at email: mmgharaibeh@just.edu.jo.
Hassan Najadat received B.Sc. of Computer Science from Muota University,
1993 and M.Sc. of Computer Science from University of Jordan, 1999 and Ph.D. of Computer
Science from North Dakota State University, 2005. Currently, he is a Professor at the
Department of Computer Information System, Jordan University of Science and Technology.
His research interests include data science, data envelopment analysis, data mining, artificial
intelligence. He can be contacted at email: najadat@just.edu.jo.
Mostafa Ali received B.Sc. of Mathematics from Jordan University of Science and
Technology, 2000 and M.Sc. of Computer Science from The University of Mitchigan, 2003
and Ph.D. of Computer Science from Wayne State University, 2008. Currently, he is a
Professor at the Department of Computer Information System, Jordan University of Science
and Technology. His research interests include artificial intelligence, deep learning,
evolutionary computation, extended reality, game theory. He can be contacted at email:
mzali@just.edu.jo.
Mwaffaq El-Heis received MBChB of Medicine and Surgery from Basrah
University in 1984, and British Fellowship of Diagnostic Radiology from Royal College of
Surgeons, 1996 and British Fellowship of Diagnostic Radiology from Royal Colleges of
Physicians of the U.K, 1996. Currently, he is a Professor at the Department of Diagnostic
Radiology and Nuclear Medicine, King M. Abdullah University Hospital. His research interests
include interventional radiology, neuroradiology, diagnostic. He can be contacted at email:
maelheis@just.edu.jo.

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