Deep Convolutional Network-Based Probabilistic Selection Approach for Multiclassification of Brain Tumors Using Magnetic Resonance Imaging

Abstract

The human brain’s computer-assisted prognosis (CAP) system relies heavily on the self-regulating characterization of tumors. Despite being extensively researched, the classification of brain tumors into meningioma, glioma, and pituitary types using magnetic resonance (MR) images presents significant challenges. Although biopsies are currently the gold standard for evaluating tumors, the need for noninvasive and accurate methods to grade brain tumors is increasing due to the risks associated with invasive biopsies. The objective is to introduce a noninvasive brain tumor grading system based on MR imaging (MRI) and deep learning (DL) utilizing probabilistic selection techniques. In the proposed method, the best three of the seven state-of-the-art deep convolutional networks are chosen after extensive experimentation and combined with a probabilistic selection technique to enhance the overall performance of the proposed classification model. The results elucidate that the proposed model successfully classifies the tumor types into Glioma, Meningioma, and Pituitary achieving a sensitivity of 0.928, 0.939, and 0.992, respectively for each tumor type. Also, the precision in classifying the tumor classes is attained as 0.969, 0.932, and 0.957, respectively claiming an accuracy of 0.966, 0.956, and 0.983 for each of the three classes. The proposed model achieved an overall classification accuracy of 96.06%, surpassing the state-of-the-art advanced and sophisticated techniques. Extensive experiments were performed on brain MRI datasets to demonstrate the enhanced performance of the proposed approach. The suggested probabilistic selection technique yielded promising classification results for brain tumors and exhibited the potential to leverage the strengths of various models.