  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.
