Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data

TL;DR

This paper introduces a hybrid CNN-SVM method with improved preprocessing for more accurate brain neoplasm detection in MRI data, effectively reducing false positives and distinguishing malignant from benign tumors.

Contribution

It proposes a novel preprocessing technique and a hybrid CNN-SVM classification approach with a modified cost function for enhanced accuracy in brain neoplasm detection.

Findings

Outperforms existing methods in accuracy

Effectively reduces false positives

Accurately classifies malignant and benign tumors

Abstract

Automatic detection of brain neoplasm in Magnetic Resonance Imaging (MRI) is gaining importance in many medical diagnostic applications. This report presents two improvements for brain neoplasm detection in MRI data: an advanced preprocessing technique is proposed to improve the area of interest in MRI data and a hybrid technique using Convolutional Neural Network (CNN) for feature extraction followed by Support Vector Machine (SVM) for classification. The learning algorithm for SVM is modified with the addition of cost function to minimize false positive prediction addressing the errors in MRI data diagnosis. The proposed approach can effectively detect the presence of neoplasm and also predict whether it is cancerous (malignant) or non-cancerous (benign). To check the effectiveness of the proposed preprocessing technique, it is inspected visually and evaluated using training performance metrics. A comparison study between the proposed classification technique and the existing techniques was performed. The result showed that the proposed approach outperformed in terms of accuracy and can handle errors in classification better than the existing approaches.