Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data
This work addresses the need for more accurate and reliable diagnostic tools in medical imaging for detecting brain tumors, though it appears incremental as it builds on established methods like CNNs and SVMs.
The authors tackled the problem of automatic brain neoplasm detection in MRI data by proposing an advanced preprocessing technique and a hybrid CNN-SVM classification method with a modified cost function to minimize false positives, resulting in improved accuracy and better handling of classification errors compared to existing approaches.
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.