Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis

Abstract

Conventional methods used in brain tumor detection, diagnosis, and classification such as magnetic resonance imaging and computed tomography scanning technologies are unbridged in their results. This paper presents a proposed model combination, convolutional neural networks with fuzzy rules in the detection and classification of medical imaging such as healthy brain cells and tumor brain cells. This model contributes fully on the automatic classification and detection of medical imaging such as brain tumors, heart disease, breast cancer, HIV, and flu. The experimental result of the proposed model shows overall accuracy of 97.6%, which indicates that the proposed method achieves improved performance over the other current methods in the literature such as classification of tumors in human brain MRI using wavelet and support vector machine (94.7%) and deep convolutional neural networks with transfer learning for automated brain image classification (95.0%) used in the detection, diagnosis, and classification of medical imaging.