A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

In healthcare sector, image classification is one of the crucial problems that impact quality output from processing domain. The purpose to categorize different images under various class labels which in turn helps detection and management diseases. Magnetic Resonance Imaging (MRI) effective non-invasive strategies generate a huge distinct number tissue contrasts every imaging modality. This technique commonly utilized by professionals for Brain Tumor (BT) diagnosis. With recent advancements Machine Learning (ML) Deep (DL) models, it possible detect tumor automatically, using computer-aided design. current study focuses on design automated Learning-based BT Detection Classification model MRI (DLBTDC-MRI). proposed DLBTDC-MRI aims at detecting classifying stages BT. involves median filtering remove noise enhance images. Besides, morphological operations-based segmentation approach also applied determine BT-affected regions brain image. Moreover, fusion handcrafted deep features VGGNet derive valuable set feature vectors. Finally, Artificial Fish Swarm Optimization (AFSO) with Neural Network (ANN) as classifier decide presence order assess enhanced performance model, comprehensive simulations was performed benchmark dataset results were validated several measures.