Deep Neuro-Fuzzy Logic Technique for Brain Meningiomasa Prediction

Simulation approaches based on Deep Learning (DL) techniques have several computational stages that present information at various levels of complexity. DL has exploded in popularity over the last decade, mainly in medical image analysis, information retrieval, and computational biology. As a result, DL has effectively transformed and enhanced detection, forecast, and treatment in various medical sectors, including pathological, brain tumors, lung disease, the stomach, heart, and eye. This study introduces a unique classification fuzzy logic-based and deep neuro (DNFLT) method compared with an SVM classifier for accurate segmentation of various brain tumors. According to the experimental observations, the suggested brain tumor classification approach is more resilient than other conventional methodologies of performance indicators, sensitivities, specificity, and correctness.