Differential Deep Convolutional Neural Network Model for Brain Tumor Classification
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Date
2021-03-10
Journal Title
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Volume Title
Publisher
MDPI
Abstract
The classification of brain tumors is a difficult task in the field of medical image analysis.
Improving algorithms and machine learning technology helps radiologists to easily diagnose the
tumor without surgical intervention. In recent years, deep learning techniques have made excellent
progress in the field of medical image processing and analysis. However, there are many difficulties
in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure
and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to
the high density nature of the brain. We propose a differential deep convolutional neural network
model (differential deep-CNN) to classify different types of brain tumor, including abnormal and
normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN
architecture, we derived the additional differential feature maps in the original CNN feature maps.
The derivation process led to an improvement in the performance of the proposed approach in
accordance with the results of the evaluation parameters used. The advantage of the differential
deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations
and its high ability to classify a large database of images with high accuracy and without technical
problems. Therefore, the proposed approach gives an excellent overall performance. To test and
train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance
imaging (MRI) images, which includes abnormal and normal images. The experimental results
showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that
the proposed differential deep-CNN model can be used to facilitate the automatic classification of
brain tumors.
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Keywords
MRI images, classification, brain tumor, differential deep-CNN, accuracy, loss values