Brain Tumor Detection using Decision-Based Fusion Empowered with Fuzzy Logic

Brain tumor is regarded as one of the fatal and dangerous diseases on planet. It present in form uncontrolled irregular cells brain an infected individual. Around 60% glioblastomas turn into large tumors if it not diagnosed earlier. Some valuable literature available diagnosis, but there room for improvement overall performance. Machine Learning (ML)-based techniques have been widely used medical domain early diagnostic diseases. The use ML conjunction with improved image-guided technology may help improving performance detection process. In this work, ML-based technique presented. Adaptive Back Propagation Neural Network (ABPNN) Support Vector (SVM) algorithms are along fuzzy logic. logic to fuse result ABPNN SVM. proposed developed using BRATS dataset. Experimental results reveal that model achieved 98.67% accuracy training phase 96.72% testing phase. On other hand, SVM has attained 98.48% 97.70% during phases. After applying decision-based fusion, reaches 98.79% 97.81% phases, respectively. comparative analysis existing shows supremacy technique.