BRAIN TUMOR CLASSIFICATION IN 3D-MRI USING FEATURES FROM RADIOMICS AND 3D-CNN COMBINED WITH KNN CLASSIFIER

http://www.iaeme.com/IJEET/index.asp 185 editor@iaeme.com
International Journal of Electrical Engineering and Technology (IJEET)
Volume 12, Issue 2, February 2021, pp. 185-198, Article ID: IJEET_12_02_017
Available online at http://www.iaeme.com/ijeet/issues.asp?JType=IJEET&VType=12&IType=2
ISSN Print: 0976-6545 and ISSN Online: 0976-6553
DOI: 10.34218/IJEET.12.2.2021.017
© IAEME Publication Scopus Indexed
BRAIN TUMOR CLASSIFICATION IN 3D-MRI
USING FEATURES FROM RADIOMICS AND
3D-CNN COMBINED WITH KNN CLASSIFIER
B. Jefferson
Vinayaka Mission Research Foundation (Deemed to be University)
Salem, Tamil Nadu, India
R. S. Shanmugasundaram
Vinayaka Mission Research Foundation (Deemed to be University)
Salem, Tamil Nadu, India
ABSTRACT
In the diagnosis of Brain tumor Magnetic Resonance Imaging has an important role
in the identification of tumor. But classification becomes very difficult for the physician
due to the complex structure of the brain. A very few features can be extracted from 2D
Brain MRI. 3D MRI provides comparable diagnostic performance and gives more
features than 2D MRI. In this paper, 3D MRI is employed for the detection and
classification of a Brain Tumor. Radiomics uses data-characterization algorithms
capable of getting a large number of features from MRI. These radiomics features can
uncover the characteristics of the disease. Pyradiomics, a python open-source package
is used to extract GLCM features. A Combinational Model that uses the features of
GLCM (Grey Level Co-occurrence Matrix) and 3D-CNN (Convolution Neural
Network) combined with KNN (K-Nearest Neighbor) is carried out on 3D MRI. 3D-
CNN is used to extract a more powerful volumetric representation across all three axes.
The last layer of 3D-CNN is supposed to learn a good representation of an image. The
features are extracted from that layer and provided to KNN classifier for further
prediction. The accuracy is observed to be improved by up to 96.7% using this method.
Keywords: 2D Brain MRI. 3D MRI, Pyradiomics, GLCM, 3D-CNN, KNN
Cite this Article: B. Jefferson and R. S. Shanmugasundaram, Brain Tumor
Classification in 3D-MRI using Features from Radiomics and 3D-CNN Combined with
KNN Classifier, International Journal of Electrical Engineering and Technology
(IJEET), 12(2), 2021, pp.185-198.
http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=12&IType=2
1. INTRODUCTION
The brain tumors are massive or abnormal cell growth in a brain region. This can spread to any
individual among different ages. A brain tumor can be classified into Malignant (cancerous)

Brain Tumor Classification in 3D-MRI using Features from Radiomics and 3D-CNN Combined
with KNN Classifier
and Benign (non-cancerous). The tumors that initially affect the brain are termed as the primary
tumors whereas the tumors that spread to brain as different parts of body are said to be a
secondary or metastatic tumor. Accurate classification is very important in saving the life of the
patients. Among many algorithms, CNN is used widely for segmentation and classification of
tumors in complex structures like Brain. More accuracy is needed in the segmentation and
classification of brain tumors to have a correct diagnosis. The variations in the pixel values
associated with the biologic structural information of the brain help 3D CNN model to
distinguish the abnormal tissues from normal tissues.
The simplest among the machine learning algorithms for classification is the K-Nearest-
Neighbour (KNN) algorithm [1]. The classification is achieved by identifying the K-nearest
matches in the training data and then using the label of nearest matches to predict. In the training
method, algorithm only stores a parameter feature vectors and labels that are needed to train the
images. At classification task, k nearest neighbours is considered in the direction of query point
without labelling. Image classification is based on labels of their K nearest neighbours. If k =
1, image is labelled from nearest class. The distance among the test data and every row of
training data is calculated with the support of the Euclidean distance calculation technique.
This manuscript utilizes a multimodal system, which merge GLCM Features [2] through
3D CNN and KNN classifier to achieve more accuracy level in classification. The 3DCNN-
KNN model has the benefits of both methods. In CNN filters are used to obtain features from
the input image. The features that are extracted will be in the form of activation maps.
Pyradiomics, a python open-source package is used to extract GLCM features. The GLCM
features and the features extracted from the last layer (Feature Layer) of 3DCNN are given to
KNN classifier for further prediction. Using 3D MR Image in 3D-CNN with the KNN model,
the brain tumor can be segmented and predicted with great accuracy.
Remaining work is mentioned as below: Section II analysis related work. Section-III
presents the methodology and architecture. Section-IV&V discusses the experimental
outcomes. Section-VI provides the conclusion.
2. RELATED WORKS
Shweta Taneja, Charu Gupta et al. [2] proposed a new enhanced algorithm for KNN. It is a
combination of dynamic selected, attribute weighted and distance weighted techniques.
Maitra and Chatterjee et al. [4] have suggested a novel method for an automated system for
magnetic resonance imaging classification. Improved version of the Orthogonal Discrete
Wavelet Transform (DWT) is used for feature extraction, known as Slantlet transform. The first
stage, the intensity histogram is calculated for each image as well as the Slantlet transformation
is applied for removing six features as intensity histogram. The second stage, a classifier based
on supervised neural network is formulated, interms of removed characteristics, for executing
the binary classification.
Zhang, Y; Wu, L; Wang, S et al. [5] have introduced a hybrid light forwarding neural
network (FNN) system for clustering brain images of MRI. This suggested approach originally
used DWT for removing the principal characteristics as MR images and then utilized the
component investigation method for reducing the feature space towards restriction. A reduced
component was sent to a forward neural network (FNN), in which the parameter was updated
by enhanced artificial bee colony algorithm (ABC).
Zhang, Y; Wu, L. et al. [6] have introduced a technique for classifying medical images
diagnosis. Few experiments were carried out with magnetic resonance imaging for the detection
of tumor. The pre-processing was performed by medium filtering process support. Then, the
necessary characteristic was removed using the texture features method. After, the extraction

B. Jefferson and R. S. Shanmugasundaram
of association rules is carried out from the removed features by the decision tree classification
algorithm.
Naik, J; Prof. Patel, Sagar et al. [7] have suggested a new technique to classify MRI images
of the brain as normal or abnormal by SVM and DWT strategy. The PCA strategy is utilized
for decreasing the number of features removed with Wavelet Transform. Such techniques have
been used to 160 MRI brain images for Alzheimer's disease detection with four dissimilar nuclei
and reached at maximal precision of GRB kernel in 9.38%.
M. Prastawa, E. Bullitt and G. Gerig et al [8] have suggested a process, which merge
physical and statistic modelling for generating synthetic multi-model 3D brain MRI through
tumor and edema. "Classification of Alzheimer's Disease Using Functional Magnetic
Resonance Data and Deep Learning Convolutional Neural Networks" discusses the challenges
of feature selection and reduction on image classification.
A. Ahlgren, R. Wirestam, F. Stahlberg, and L. Knutsson et al [9] have introduced a
segmentation method of human brain by structural magnetic resonance image was the main
steps of neuroscience imaging. This is mainly based on expressive and discriminative power
contained in the feature vectors. These feature vectors removed as image data were utilized for
training the classification model. For numerous years, investigations have studied artificial
neural networks (ANNs) for solving complex image classification issues.
N. Nabizadeh, N. John, and C. Wright [10] have suggested an automated algorithm for
detection and segmentation of strokes and tumor lesions using single-spectrum magnetic
resonance imaging. Here, four categorization models (Naive Bayes, Naive Bayes based on
multivariate filters, Naive Bayes of selective filters and support vector machines) have been
used for accessing its ability for discriminating among patients with Parkinson's disease (PDCI),
PDMCI and PDD.
V. G. Kanas, E. I. Zacharaki, et al, have performed an inside and outside the evaluation of
arrangement of techniques. These techniques are investigated utilizing data mining algorithms,
for example, fp-tree-based association mining, and apriori, k-means clustering, k-nearest
neighbor classifier, and decision tree structure [11].
Hanh Vu, Hyun-Chul Kim et al [12] have presented the statistic importance were assessed
using t test paired. Generally, 3D-CNN portrays reduced error rates compared with fcDNN and
SVM. In particular, 3D-CNN through ReLU trigger function displayed smallest average error
rate, in particular, AD task fault rate (6.4 ± 2.7%) was considerably reduced from 3DCNN with
ReLU to fcDNN models. The 3D-CNN and fcDNN LH, RH, and VS error rates with ReLU
were comparable.
Guotai Wang, Wenqi Li, et al [13], have introduced the enhanced tumor core was segmented
depend on tumor core segmentation bounding box of outcome. Networks have dilated multiple
layers as well as anisotropic convolution filters, they combined through multi-view fusion for
decreasing false positives. Residual connections and multi-scale forecasts were used at such
networks for driving segmentation execution.
M Malathi, P Sinthia et al [14], have introduced the entirely automatic segmentation of brain
tumor by convolutional neural network. Between brain tumors and gliomas were the most
general destructive, foremost towards little life suspense on its greatest grade. At medical
practice, manual segmentation was time-consuming task as well as its execution is greatly
dependent on experience of the operator.
J. Seetha and S. Selvakumar Raja et al [15], have introduced the enormous amount of data
created by MRI frustrates the manual classification of tumor vs non-tumor at an exacting time.
But it has few restrictions (i.e. precise quantitative measurements were given for a restricted

with KNN Classifier
number of images). This introduce the automatic detection of brain tumors using the
classification of convolutional neural networks (CNN).
3. PROPOSED METHODOLOGY
In the proposed method, a combinational algorithm is used for classification. The 3D image is
pre-processed by removing noise and the bias field is corrected using N4ITK. The features are
extracted by 3D CNN and GLCM methods [16]. The features are then provided as input towards
KNN classifier for further prediction.
The proposed method utilizes the python code to implement the detection and classification
of MRI brain tumor. Python programming has been chosen to implement the proposed model.
Python code is more compact and readable. And it is an open-source and also provides more
graphic packages and data sets.
Hence, the proposed work utilizes python programming instead of MATLAB. Additional
python packages like NumPy, scipy, scikit-learn, TensorFlow, and Keras are used in the
implementation of this proposed system.
3.1 Datasets
In our model, 3D images of the NIFTI format are used. The dataset contains 300 images each
of size 155*240*240. It consists of 125 benign images, 150 malignant images and 25 normal
images. Dataset is split into two groups: 70% for training, 30% for testing. This datasets
training and testing using an advanced learning algorithm for the segment of tumor from the
three-dimensional (3D) Magnetic Resonance (MR) input images, that images are labelled using
T1, T2, and Flair (F) modalities of the dataset. Our implementation is with 3D brain tumor
classification using NII images.
3.2 Model Architecture
Figure 1 Proposed Architecture

3.3 Pre-Processing
The 3D MRI images are enhanced and prepared so as to support for the python modules in this
pre-processing phase. The pre-processing stages are image resizing and noise removal. These
two stages are using Median filters [17] to get perfect input image
Figure 2 Input 3d MRI Image
X-Axis Image
Y-Axis Image
Z-Axis Image
Figure 3 Reshaped Images

with KNN Classifier
Image resizing refers to the scaling of images. Resizing helps to reduce the number of pixels
in image and it contain many benefits e.g. this will decrease the training time of neural network
and reduce complexity of the model.
OpenCV-Python provides several interpolation methods that helps in resizing an image.
The resizing can be done using scipy and ndimage that provides functions operating on n-
dimensional NumPy arrays. The python package scipy.ndimage gives wide range of functions
to process multi-dimensional images. The Reshaped matrix is 120*120*77.Median filter is used
to remove the noise form 3D MRI Image. A filter with 3x3x3 dimension is used in the
algorithm. The information related to edges in the images are retained. The Median filter
algorithm gives a resultant new image by replacing the median of neighbouring pixels.
Figure 4 Noise Image
Figure 5 Noise Removal Image
Figure 6 Histogram of Original and Noise image
Algorithm of median filter is below:
• Choose three-dimensional window size 3*3*3.

• Cx,y,z is the pixel to be processed
• The median of pixel values (Pmed) in 3D window
• is calculated
• Cx,y,,z replaced with Pmed.
• Redo steps 1 to 3 till each and every pixel in the whole image are processed.
3.4 Feature Extraction Using 3D-CNN
3.4.1 3D-CNN Architecture used
In 3D-CNN model, convolution + pooling layers perform feature extraction. 3D-CNN contain
3D convolution layers (Conv) (8 filters with a core size of 7 × 7 × 7 at initial convolution layer,
16 filters with a core size of 5 × 5 × 5 at second convolution layer and 32 filters along core size
of 3 × 3 × 3 on third convolution), and two completely connected layers (128 hidden nodes on
hidden layer). Moreover, an output layer through four output nodes for classifying every one
of four rules.
The 3D volume input is 120 * 120 * 77, the output pattern dimension in initial convolution
is 38 * 64 * 6 * 160 based on the step of two then the convolution work and it has eight of them
through 5 filters; the output pattern dimension on second layer of conv. is 17 * 30 * 2 * 320 as
step of two and it contain 16 of them on 16 channels; after that output of convolution layer was
altered to a 1-dimensional vector along 81,674,402 elements and this utilized as input of entirely
associated layer. In the proposed work GLCM and CNN inputs are linked through KNN
Classifier, Using some KNN packages in my python code K-Neighbors Classifier is used as
sklearn model choosen import train_test_split, as sklearn import pre-processing, neighbors.
GLCM-CNN topology has an types of layers are the convolutional layer (conv3d_1, conv3d_2),
subsampling layers (maxpool1, maxpool2), and an output layer (dense_5).
3.4.2 Training of 3D CNN Model
The parameters utilized for training 3 dimension convolution network mentioned below:
activation function of rectified linear units of every node on convolution layers and FC layer;
stochastic gradient descent through the primary learning rate of 10-3
(no impulse) as well as
annealing subsequent to 20 epochs through minimal learning rate of 10-6
; the mini batch size
32; withdraw through 0.5 on third 3 dimension convolution layer for reducing over fitting. In
addition, RELU trigger function also applies for 3DCNN. The cross-validation framework of
leaving a subject out is utilized for assessing performance
Figure 7 Output of conv2

with KNN Classifier
(i.e., 210 subjects utilized to train 3D-CNN as well as rest of the subject to experiment the
trained 3D-CNN and redo this progression for every one of 12 subjects from test subject).
Figure 8 Output of conv3
Figure 9 3D-CNN Model Summary
3.4.3 GLCM Feature Extraction using PyRadiomics
Radiomic may be used for most imaging procedures, include X-ray, ultrasound, CT, MRI, and
PET studies. It may be utilized for increasing the accuracy on diagnosis, prognostic assessment,
and prediction of response to therapy, mainly in combination with clinical, biochemical, and
genetic data. The purpose of the most discriminating radiological feature extraction techniques

will vary with the imaging modality and the pathology studied and, currently the focus of
investigation in the radiomics field.
GLCM Features contain volume, shape, surface, density, and intensity, texture, location,
and the relationships to surrounding tissues. The radiology lexicon usually has semantic
features for describing areas of interest. Cognitive features that attempt for capturing the
heterogeneity during quantitative interpretations.
The Radiomic features were described based on Pyradomics Python package, version 1.2.0.
Feature extraction contains 18 initial-order, 17 shapes and 56 texture features. Initial row and
layout characteristics are computed based on eight wavelet distortions, outcoming at 287 total
image features. Very few numbers of features are used in this paper which are as shown in
Table2.
Table 1 GLCM Features using Pyradiomics
Features Values
Energy 2918821481.0
Entropy 4.920992838328338
Kurtosis 2.1807729393860265
Mean 825.235436306502
Skewness 0.27565085908587594
Contrast 74.04325876559685
Correlation 0.39322090748573196
Strength 0.9828367173152485
Shape 0.5621171627174117
Surface 6438.821603779402
Density 54.27945170740796
Intensity 0.27565085908587594
Texture 17.33
3.5 CNN Classifier
The training instances are multidimensional feature space with vectors, through a class label.
At classification stage, k is characterized by user-defined constant, and the unnamed vector is
categorized via the label that often occurs in k training samples nearest to query point. A
normally used distance metric for incessant variables is Euclidean distance. At context of
genetic expression micro array data, for instance, K-NN is used as a metric with correlation
coefficients like Pearson and Spearman. Often, the classification accuracy of K-NN may be
extensively improved by learning a distance metric through special algorithms like analysis of
neighboring or neighboring elements near large margins. Receptive field for removed 3D
feature maps. To reset the spatial information of 3D feature maps, we introduce the layer
synchronization framework into the model. Bilinear interpolation is utilized to perform the up-
sample function. Bilinear interpolation is an addition of two-dimensional rectangle. The latter
process is used at effort for solving the issue of misclassified voxels. To treat the tumor through
smallest linked area as misclassified voxel that is unnoticed through the threshold technique,
i.e. remove the malignant tumor area with least attached area to a pre-defined threshold. The
pre-defined threshold is set to one tenth of maximal area connected. This is what we need to
create a prediction. We may utilize the output values directly as the probability belonging to
every output class. This function is known as predictive method, which realize this procedure.
It provides code in network output with the greatest probability. It considers that class values
start at 0 and are converted to integers.

with KNN Classifier
4. IMPLEMENTATION
The proposed method is implemented using the Keras tensor flow library in the python
environment. Jupyter notebook and python script are used. Training Data was generated by
taking 300 random samples from each brain MRI study Data is duplicated by applying rotation
and flipping operation to make it unbiased. In table 3, the total number of the data sample used
in the training dataset is 307. The model was trained into 50 epochs by a batch size of 32. The
binary cross-entropy loss function is used with Adam optimizer. This time period taken to train
the model is based on system performance and configurations. The current implemented model
has a 0.5% training loss while a 1.2% testing loss which can be improved by changing the model
parameters.
Table 2 Dataset Partition for Implementation
Types of Images Samples
Total No of images 300
No of malignant images 150 images
No of Benign images 125 images
No of Normal images 25 images
Tool used Anaconda jupyter notebook4
5. RESULT AND DISCUSSION
The outcomes of the evolved CNN are displayed at Table 4 and displayed by confusion
matrices, displayed at Figure-12. In confusion matrices, non-white rows indicate classes of
network output and Non-white columns relate to actual classes. The numbers / percentages of
exactly classified images are displayed at dark coloured diagonal. The final row indicates the
sensitivity, while the final column relates to the specificity. The overall precision is displayed
at lower right field. It is then classified by KNN subsequently error prevention algorithm is
performed. This can support in distinguish tumor cells from usual cells. Small kernel size helps
us in getting a deep network and hence getting better details of the features. The model can be
tested on any database as the spatial alignment is preserved by the multistage approach.The
error rate is very much reduced in the K value range of 10 to 20. KNN is performing well with
the combinational features from GLCM (Handcraft features) and CNN (non-handcraft
features).
Figure 11 Error Rate

The confusion Matrix based on percentage for the proposed system is given in Fig 12. With
this confusion matrix, the evaluation metrices like precision, recall, F1score and accuracy are
calculated.
Figure 12 Percentage based Confusion Matrix
The accuracy and Recall value for the proposed method is greater compared to 2D CNN-
KNN, 2DCNN and KNN. The combinational features play great role in deriving the accuracy.
Table 3 Comparison Between 2D and 3D Models
Model
Precisio
n
Recall F1 Score
Accurac
y
Ref
3D CNN-KNN
(proposed)
98.17 96.78 97.47 96.74
Our
Model
2D CNN-KNN 96.67 95.83 96.25 96.25 [18]
2D CNN 97.50 94.17 95.81 94.39 [19]
KNN 95.30 93.66 94.33 89.5 [20]
The positive predictive value which represents the precision, is high in higher dimensional
perspective. It is the ratio of true positive to the sum of true positive and false positive. The
proposed model is having a precision of .98.
Predicted
values
Negative
Positive
Actual Values
Positive Negative
62.99 %
True Positive
1.17%
False Positive
2.09%
False Negative
33.75%
True Negative

with KNN Classifier
Figure 13
The accuracy comparision and the F1 Score comparision are shown in the graph –
Figure.13&14. Our Modal 3D CNN-KNN gives more accuracy and F1Score than other modals.
Figure 14
6. CONCLUSION
In this paper a combinational feature of both handcrafted features (HCF) and non-handcrafted
features (NHCF) of 3D MRI extracted using GLCM and 3D CNN are used to get more
improved accuracy. Pyradiomics of python is used for the extraction of HCF from GLCM. The
feature layer of CNN gives a better details about the features. Both the features are fed to KNN
to classify the output classes. The metrics like accuracy, recall, F1 score are calculated using
confusion matrix that evaluates the performance of the model. Since both HCF and NHCF are
used, KNN get the advantages of both and the prediction gives 96.7 percentage accuracy.
94
94.5
95
95.5
96
96.5
97
3D CNN-KNN 2D CNN-KNN 2D CNN KNN
Accuracy Comparision
92.5
93
93.5
94
94.5
95
95.5
96
96.5
97
97.5
98
3D CNN-KNN 2D CNN-KNN 2D CNN KNN
F1 Score Comparision

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AUTHORS PROFILE
B.Jefferson, has received M.Sc physics in 1997, M.Sc
Software Technology & Management in 2001 and M.Tech
computer science & IT in 2009 from Manonmaniam
sundaranar University, Tirunelveli, TamilNadu, India. He is
currently a research scholar working toward the PhD degree
in the Department of Computer Science, VMKV Arts and
Science College, Salem. His research interests are in Medical
image processing, Medical image analysis and Image
Classification
Dr.R.S.Shanmugasundaram, has received the BE degree
in Electronics and Communication Engineering from the
university of Madras of India in 1996, ME degree from the
Bharathidasan University of India in 2001 and Ph.D degree
from Anna University of India in 2015. His research interests
are in medical image processing, deformable models and
segmentation. He is a life member of ACS and ISTE.
Currently he is working as Deputy Director (Academics) of
Vinayaka Mission’s Research Foundation (Deemed to be
University), Salem, TamilNadu

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