Brain Tumor Detection Using Convolutional Neural Network

Abstract

Brain Tumor segmentation is one of the most crucial
and arduous tasks in the field of medical image
processing as a human-assisted manual
classification can result in inaccurate prediction
and diagnosis. Moreover, it becomes a tedious task
when there is a large amount of data present to be
processed manually. Brain tumors have diversified
appearance and there is a similarity between tumor
and normal tissues and thus the extraction of tumor
regions from images becomes complicated. In this
thesis work, we developed a model to extract brain
tumor from 2D Magnetic Resonance brain Images
(MRI) by Fuzzy C-Means clustering algorithm
which was followed by both traditional classifiers
and deep learning methods. The experimental study
was carried on a realtime dataset with diverse tumor
sizes, locations, shapes, and different image
intensities. In traditional classifier part, we applied
six traditional classifiers namely- Support Vector
Machine (SVM), K-Nearest Neighbor (KNN), Multilayer
Perceptron (MLP), Logistic Regression, Naive
Bayes and Random Forest. Among these classifiers,
SVM provided the best result. Afterwards, we moved
on to Convolutional Neural Network (CNN) which
shows an improvement in performance over the
traditional classifiers. We compared the result of the
traditional classifiers with the result of CNN.
Furthermore, the performance evaluation was done
by changing the split ratio of CNN and traditional
classifiers multiple times. We also compared our
result with the existing research works in terms of
segmentation and detection and achieved better
results than many state-of-the-art methods. For the
traditional classifier part, we achieved an accuracy
of 92.42% which was obtained by Support Vector
Machine (SVM) and CNN gave an accuracy of
97.87%.