JOIV : Int. J. Inform. Visualization, 6(2-2): A New Frontier in Informatics - August 2022 581-586

INTERNATIONAL JOURNAL
ON INFORMATICS VISUALIZATION

INTERNATIONAL
JOURNAL ON
INFORMATICS
VISUALIZATION

journal homepage : www.joiv.org/index.php/joiv

Deep Learning Approach for Prediction of Brain Tumor from Small
Number of MRI Images
Zulaikha N.I. Zailan a, Salama A. Mostafa a,1, Alyaa Idrees Abdulmaged b, Zirawani Baharum c,2,
Mustafa Musa Jaber d, e, Rahmat Hidayat f
a

Center of Intelligent and Autonomous Systems (CIAS), Faculty of Computer Science and Information Technology,
Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia
b
Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia Pagoh Campus, 84600 Pagoh, Johor, Malaysia
c
Malaysian Institute of Industrial Technology, Universiti Kuala Lumpur, Persiaran Sinaran Ilmu, 81750 Johor Bahru, Malaysia
d
Department of Medical instruments engineering techniques, Dijlah University College, Baghdad,10021, Iraq
e
Department of Medical instruments engineering techniques, Al-Farahidi University, Baghdad,10021, Iraq
f
Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia
Corresponding author: 1salama@uthm.edu.my, 2zirawani@unikl.edu.my

Abstract— Daily, the computer industry has been moving towards machine intelligence. Deep learning is a subfield of artificial
intelligence (AI)'s machine learning (ML). It has AI features that mimic the functioning of the human brain in analyzing data and
generating patterns for making decisions. Deep learning is gaining much attention nowadays because of its superior precision when
trained with large data. This study uses the deep learning approach to predict brain tumors from medical images of magnetic resonance
imaging (MRI). This study is conducted based on CRISP-DM methodology using three deep learning algorithms: VGG-16, Inception
V3, MobileNet V2, and implemented by the Python platform. The algorithms predict a small number of MRI medical images since the
dataset has only 98 image samples of benign and 155 image samples of malignant brain tumors. Subsequently, the main objective of
this work is to identify the best deep learning algorithm that performs on small-sized datasets. The performance evaluation results are
based on the confusion matrix criteria, accuracy, precision, and recall, among others. Generally, the classification results of the
MobileNet-V2 tend to be higher than the other models since its recall value is 86.00%. For Inception-V3, it got the second highest
accuracy, 84.00%, and the lowest accuracy is VGG-16 since it got 79.00%. Thus, in this work, we show that DL technology in the
medical field can be more advanced and easier to predict brain tumors, even with a small dataset.
Keywords—Brain tumor; magnetic resonance imaging; deep learning; image classification.
Manuscript received 10 Nov. 2021; revised 25 Feb. 2022; accepted 12 Apr. 2022. Date of publication 31 Aug. 2022.
International Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

samples of benign and malignant brain tumor data to be
compared in the classification [4]–[7]. The principle of deep
learning should have a minimum of 1000 images per class for
image classification [8]. The problem is how to configure a
model to deal with fewer numbers of brain tumor data. Deep
learning makes it very challenging to predict brain tumor
cases when the sample data is small. The dilemma of a small
number of samples causes the algorithm not to learn very well
and, in the end, not produce a significant result.
A vast number of researches have been dedicated to using
modern technologies such as machine learning (ML) and deep
learning (DL) in the medical fields [9]–[12]. Six papers have
been discussed in this section. Based on the previous projects,
two projects use the same data set as the current project [13],

I. INTRODUCTION
Deep learning is a type of artificial intelligence (AI) that
mimics the functioning of the human brain in analyzing data
and generating patterns for making decisions [1]. As said in
[2], the importance of deep learning is that the potential to
analyze large numbers of characteristics makes deep learning
more efficient when facing unstructured data, but the data to
be used should be in a large amount to make the deep learning
algorithms a very powerful predictor.
A brain tumor is an anomalous cell that grows in the brain
[3]. Usually, the deep learning approach is used to predict
brain tumors from many medical images. The dataset has

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[14]. The previous study conducted three algorithms: Resnet50, Inception-V3, and VGG-16 [15]. The dataset obtained
from Navoneel is trained and tested in this work. The dataset
consists of 253 brain MRI samples, 155 malignant tumor
samples, and 98 benign tumor samples. The implementation
covers various measurements and different aspect ratios in the
crop normalization process. The pre-training model requires
the image dimensions to be 224 × 224 × 3. Hence, the images
have to be resized to a predefined format. Following the
analytical findings, the Resnet-50 obtains maximum test
accuracy of 95% with a zero-negative rate. In contrast, the
VGG-16 achieves a decent accuracy of 90%. In conclusion,
they suggest that hyperparameter adjustments and better
processing techniques can be made [16].
In [14], the models that are applied in the implementation
of transfer learning are VGG-16, ResNet-50, and InceptionV3. Backpropagation learning trains knowledge in pattern,
sequential or incremental mode, and batch mode. It needs less
local storage, a quicker method, and appears to be captured at
the local minimum. It handles online learning and stops it
after a certain time has elapsed or when mistakes surpass the
required amount. Subsequently, they use a supervised
network or reinforcement learning intending to improve the
training of their models. Among the Keras versions, ResNet50 scores the best overall accuracy, and models' resolution has
the best validation accuracy of 91.09%. Also, processing the
dataset without transfer learning consumes 40 minutes, while
transfer learning consumes 20 minutes (i.e., 50% lesser time).
Brain tumor Isocitrate dehydrogenase (IDH) status was
identified via DL algorithms [17]. This study uses 52 subjects
as their test dataset and three ML models, ResNet-50,
DenseNet-161, and Inception-v4, to predict IDH status. Based
on their findings, the three models pass the 5-folds
preparation, testing, and validation. In this study, the
suggested models aim to provide limited preprocessing and
achieve high accuracy without tumor segmentation or
appealing regional extraction. The DenseNet-161 model
excels the results of both ResNet-50 and Inception-v4 models
by scoring 90.5% accuracy with a standard deviation of ±
1.0% and AUC of 0.95. Unlike the ResNet-50 model, the
DenseNet-161 platform architecture carries the data from all
previous layers and adds the information to the next layer.
This procedure helps to understand the data from various
layers before passing it to the next layers. Acknowledging the
topic of dealing with data leakage is their biggest contribution.
The volumetric and location characteristics are retrieved
from each segmentation file given in the dataset in the first
stage [18]. Third, the histogram features were extracted and
utilized to train the ML algorithms to classify the data. These
combined attribute vectors with labels are used to train
various ML algorithms. Overall, the absence of classification
accuracy is impaired by the observation of numerous
undesired classification classes, as shown in Table I. The
extracted data represents the statistical and strength texture
features, and several ML are trained using texture features.
With 10-fold specificity, the classification accuracy based on
three classes did not exceed 46.0%. The classification
precision increased to 65.0% when K-Nearest Neighbors
(KNN) and Support Vector Machine (SVM) classifiers were
used.

TABLE I
RELATED WORKS' COMPARISON

Ref.

Dataset

[15] 253 samples (155
malignant and 98
benign)
[1] 253 samples
(155 malignant
and 98 benign)
[17] 52 samples

[18] 163 samples

[19]
[20]

100 samples
3 064 samples

Model
VGG-16
Inception-V3
ResNet-50
VGG-16
ResNet-50
Inception-V3
InceptionSlice wise
v4
Subject wise
ResNet-50 Slice wise
Subject wise
DenseNet- Slice wise
161
Subject wise
SVM
Linear SVM
Coarse
Gaussian
SVM
KNN
Linear Discriminant
RUS-Boosted Trees
Logistic Regression
Deep Q Network (DQN)
CNNs
BayesCap
Bayesian
CNN
CapsNet

Accuracy
(%)
90.0
55.0
95.0
60.0
80.0
50.0
76.1
64.2
89.7
81.4
90.5
83.8
48.5
49.0
50.9
68.8
62.5
68.8
85.0
73.6
71.3
68.3

Reinforcement learning is used in a Deep Q Network
(DQN) model to process the MRI data [19]. The data
collection of 100 images is used for testing Brain Lesions
based on MRI, of which 70 images are used for training and
30 images for testing. The success rates in these reinforcement
learning predictions reliably increase during testing, while
deep learning results tracked easily deviate. Reinforcement
learning is estimated by testing setting lesion positions with
an accuracy of 85.0%, compared to an accuracy of about 7.0%
for the supervised deep network. In conclusion, lesions can be
predicted highly with reinforcement learning for such a
limited training set.
Integrating Capsule networks (CapsNets) was proposed
with Bayesian algorithms to create a BayesCap model [20]. It
uses a dataset of 3,064 brain MRI images to test the proposed
BayesCap, Bayesian CNN, and CapsNets for validation. The
diagnosis includes one of the three tumor types: meningioma,
glioma, and pituitary. 80% of the data is selected by replacing
the training set on each bootstrap used. A total of 30 iterations
are performed to assess the general probability of the
CapsNets as well as calculate the 95% interval confidence
(CI) of the results. Using the same data as the proposed
BayesCap model, non-Bayes CapsNets are also trained,
resulting in lower precision than the initial precision obtained
after 500 times which is 68.3% with CI (67.1%, 69.5%). With
a group size of 16, the models in each bootstrap were
practiced 500 times. However, this accuracy is less than 78%
obtained using the whole brain MRI images from nonBayesian CapsNet, which is predicted because the Bayesian
variant is targeted at studying the back of model weights and
catching uncertainty instead of increasing accuracy. By
gathering more image data, such variability can be decreased.
The results demonstrated in this analysis that the precision

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could be steadily increased by filtering out the unknown
forecasts at various thresholds. Table I shows the summary of
each related work and its accuracy rate.
In short, it shows that the DenseNet-161 is the best
approach that can be used in DL prediction research for a
small dataset by achieving an accuracy of 90.5%. On the other
hand, the Inception-V3 is an unsuitable model to train and test
the small size of datasets. This is further strengthened by the
findings of [1] and [15], in which Inception-V3 achieves
50.0% accuracy and 55.0%, respectively.
The objectives of this study are defined in two points: to
apply three types of DL models: VGG-16, Inception-V3, and
MobileNet-V2 on brain tumor datasets of MRI images and to
assess and analyze the performance of the DL models using
accuracy, precision, and recall among other criteria.
Therefore, this study uses three DL algorithms implemented
in Python to predict brain tumors from a small set of medical
images of MRI type.

Business understanding is a phase that requires
understanding the business objectives and turning them into
data mining problems and developing project plans. The
project background should be investigated from various
sources to determine the satisfaction of the business
objectives. In this study, business understanding is referred to
as a project of automating the tumor type diagnosis since it
does not involve real industry. We defined the project's
objectives at this stage: (i) to implement three types of deep
learning models: VGG-16, Inception-V3, and MobileNet-V2
on MRI image datasets of brain tumors, and; (ii) to test and
compare the prediction of the tumor images using accuracy,
precision, and recall parameters based on the results of the DL
models. Subsequently, the data understanding stage begins
with the compilation of initial data and access to the dataset
to explore, describe, examine, and verify its usefulness and
quality.
Data preparation is a state of deciding on the dataset, the
collection of features to be considered, and the suitable
partitioning of the dataset. The dataset to be applied is
examined based on data analysis, and numerous
preprocessing steps are implemented to verify its content and
make it ready for running. The data preparation phase
involves selecting, cleaning, constructing (data attributes),
reformatting, and integrating (merging the data). Since the
data of this work is an image, and the method used is DL, it
does not need to be numerically prepared, and only the quality
of the image should be examined.
Thereafter, the relevant modeling methods, algorithms, or
combinations are chosen and prepared in the modeling phase.
The setting values of the parameters of the algorithms are also
determined. The algorithms to be used in this phase are VGG16, Inception-V3, and MobileNet-V2. The performance of the
algorithms is formally measured based on accuracy,
precision, and recall as standard data mining criteria. The
processes of this phase are always carried out in an iterative
way in which the selected model consistently operates to
satisfy the testing requirements. The performance result for
each algorithm is summarized, and which method has the
highest performance is identified. Before the final
implementation, a scientifically high-quality model has
already been identified in the development phase. Lastly, it is
important to analyze the product's final implementation
closely. The best model development measures should be
revised and ensure that market priorities are properly
achieved.

II. MATERIAL AND METHOD
Data mining is a popular trend for exploring ML and DL
methods and techniques to discover new patterns and
knowledge from different data sources. Model building and
pattern recognition are two fundamental elements of data
mining, and much of its emphasis is on filtering and reducing
data. While certain sub-disciplines of statistics have studied
special cases of this problem, most of the data mining work
on pattern detection to date has been computational and
focuses on Improving and developing algorithms.
The CRISP-DM methodology is produced by a consortium
effort originally formed with DaimlerChrysler, SPSS, and
NCRR [21]. CRISP-DM stands for CRoss-Industry Standard
Process for Data Mining, a common technique to boost the
performance of Data Mining ventures nowadays [22]. This
methodology also defines a project as a cyclic process where
it can help support business decisions by defining a non-rigid
six-phase process, as shown in Fig.1. It facilitates the creation
and application of data mining models to be used in real-world
settings.

A. Brain Tumor MRI Dataset
The dataset used in this study was taken from the Kaggle
website, namely Brain Tumor MRI Images for Brain Tumor
Detection by Navoneel Chakrabarty [24]. The total number of
samples was 253, of which 98 were benign samples while 155
images were malignant brain tumor samples. Fig. 2 (a) shows
"No" samples (benign), and Fig. 2 (b) shows "Yes" samples
(malignant). The resolution of each image is between 88x88
dpi to 300x300 dpi, and they are 2-dimension (2D) images.

Fig. 1 The phases of the CRISP-DM method [23]

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because of Inception's unique architecture. As a result, this
model is commonly used for transfer learning [29].
3) MobileNet-V2: MobileNet is a CNN-based model that
is commonly used to recognize objects in pictures. As
illustrated in Figure 3, the fundamental advantage of
employing the MobileNet architecture is that it requires
significantly less computing effort than the classic CNN
model, making it perfect for usage on mobile devices and PCs
with lower processing capabilities [31]. MobileNet is built on
a complex design. The core structure is built on top of
numerous levels of abstraction, which are components of
various convolutions that appear to be quantized
configurations that thoroughly examine the complexity of
ordinary situations [32]. Point-wise complexity refers to the
complexity of 1x1 points. The depth infrastructure is based on
an abstraction layer that points the depth structure through a
corrected standard linear unit (ReLU). With the same
variable, the resolution multiplier is used to lower the
dimensions of the input picture and the internal representation
of each layer.

Fig. 3 CNN Architecture [33]

C. Evaluation Metrics
All values of two attributes are considered in this
preprocessing classification: tumor and grey matter features.
The classification accuracy of VGG-16, Inception-V3, and
MobileNet-V2 algorithms is compared at the end of this
study. In addition, a confusion matrix containing True
Positive, True Negative, False Positive, and False Negative
values will be used to calculate the values of accuracy,
precision, and recall parameters in this study [26]-[33].

Fig. 2 Samples of brain tumor MRI dataset [24]

B. Deep Learning Algorithms
This section outlines the three DL algorithms to be used in
this image classification experiment: VGG-16, Inception-V3,
and MobileNet-V2.
1) VGG-16: VGG-16 is a convolutional neural network
that has 16 layers. It was proposed by Simonyan and
Zisserman from the University of Oxford in their paper "Very
Deep Convolutional Networks for Large-Scale Image
Recognition" [25]. Using the VGG-16 network in this
experiment, the pre-trained VGG-16 coevolutionary neural
network model was adjusted to prevent overfitting by freezing
some layers due to the small dataset [26]. It contains 16 layers
of Convolution in the network, and eventually, the layers will
be interconnected with the SoftMax output layer, which
strengthens the ability to learn hidden features [27].

1) Accuracy: Accuracy is the number of correctly
predicted data points from all the datasets. Where TP, TN, FP,
and FN stand for True Positive, True Negative, False Positive,
and False Negative.
(1)

=

2) Precision: Precision is the total number of positives
out of all clearly defined positives. Where TP is True Positive,
meanwhile FP is False Positive.

2) Inception-V3: A basic convolution block replaces the
convolution layer and maximum aggregation for feature
extraction. Inception-v3 frequently employs conventional
kernels to reduce the number of feature channels and speed up
training. Because the basic convolution block, the enhanced
Inception module, and the classification are three elements of
the Inception-V3 model [28], the object recognition
performance of the Inception-v3 model is superior. Finally,
Inception-v3 boasts the best object recognition performance

=

(2)

3) Recall: The recall is the same as sensitivity, meaning
the ratio between the total number of positive outputs was
clearly defined as positive to the actual positive amount.
Where TP is True Positive, while FN is False Negative.
=

584

(3)

4) F1 Score: F1 score is considered a better predictor of
the performance of the classifier than the standard accuracy
test.
1

=

!

"

! "

#$$

(4)

#$$

III. RESULT AND DISCUSSION
In this section, the research design and implementation of
the project have been addressed in depth. Three DL
algorithms are tested for this initiative, namely VGG-16,
Inception-V3, and MobileNet-V2. Python Language,
TensorFlow. Keras is used as the main tool for the testing
backbone. The brain tumor MRI image data is divided into 70
percent training and 30 percent testing. Based on Table II
below, the confusion matrix shows that the MobileNet-V2
model, 26, makes the highest True Positive, and the lowest
True Negative is MobileNet-V2, 18. The lowest False
Positive belongs to Inception-V3 since it has one positive and
the highest False Negative, 46. Table II also shows the
confusion matrix of each model.

VGG-16

Actual
values

MobileNetV2

Actual
values

Inception-V3

Actual
values

Positive
Negative
Total
Positive
Negative
Total
Positive
Negative
Total

24
11
35
26
8
34
18
1
19

5
36
41
26
8
34
18
1
19

Total

Negative

Confusion
Matrix

Positive

TABLE II
THE CONFUSION MATRIX

29
47
76
29
47
76
29
47
76

Table III shows the result of sensitivity, specificity,
precision, recall, f1-score, and accuracy for VGG-16,
Inception-V3, and MobileNet-V2. The test result shows that
the highest accuracy for MobileNet-V2 is 86.00%, For
Inception-V3, it got the second highest accuracy of 84.00%,
and the lowest accuracy is VGG-16 since it got 79.00%.
Subsequently, Fig. 4 illustrates the accuracy and error changes
over the 100 epochs employed. It is to be observed that the
accuracy has risen to almost 80% after the 20th epoch.

Fig. 6 Graph Learning Rate of the three models

IV. CONCLUSION
This paper presents the diagnosis of a brain tumor based on
MRI images. The diagnosis is performed using three DL
algorithms, VGG-16, Inception-V3, and MobileNet-V2. The
dataset of MRI brain tumor images is used for testing and
evaluating the three algorithms. Consequently, the main
objective of this work is to identify the best DL algorithm that
performs on small-sized datasets. The test result shows that
the VGG-16 achieves an accuracy score of 79.00%,
Inception-V3 achieves an accuracy score of 84.00%, and
MobileNet-V2 achieves an accuracy score of 86.00%. The
main constraint addressed in this project is that the dataset has
a small amount of data, which results in insufficient learning
from the supposed amount of image samples. Nevertheless,
the MobileNet-V2 is found to be the best algorithm among the
three to deal with such constraints. In the future, the project

TABLE III
THE ACCURACY RESULTS OF THE VGG-16, INCEPTION-V3, AND
MOBILENET-V2

Item

Parameter

VGG16 (%)

InceptionV3 (%)

MobileNetV2 (%)

1

Sensitivity

82.76

62.07

89.66

2

Specificity

76.60

97.87

82.98

3

Precision

78.50

88.0

84.50

4

Recall

80.0

80.0

86.50

5

F1-score

78.50

81.50

85.50

6

Accuracy

78.95

84.21

85.53

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will be improved by using some well-known feature selection
methods to select the most useful features. The feature
selection mechanisms can improve the performance of the DL
algorithm.

[16]

[17]

ACKNOWLEDGMENT
This paper is funded by the Research and Innovation,
Universiti Kuala Lumpur. This paper is supported by the
Center of Intelligent and Autonomous Systems (CIAS),
Universiti Tun Hussein Onn Malaysia (UTHM).

[18]

[19]

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