Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

disorders are a significant source of economic strain and unfathomable suffering in modern society. Imaging techniques help diagnose, monitor and treat mental health, neurological, and developmental disorders. To aid in the Computer-Aided Diagnosis of brain diseases, deep learning (DL) was used for the analysis of neuroimages from modalities Magnetic Resonance Imaging (SMRI), and functional MRI. In this study, a Whale Optimization Algorithm is used with Deep Learning to analyse MRI scans for signs of neurological disease. WOADL-BDDC may detect and label abnormalities in the brain based on an MRI scan. It uses a two-step pre-processing procedure, first using guided filtering to get rid of background noise and then using U-Net segmentation to get rid of the top of the head. QuickNAT, along with RMSProp, is used to segment the brain. When analysing data, WOADL-BDDC uses radionics to collect information from every layer. When used in a convolutional recurrent neural network model, the Whale Optimization Algorithm may accurately categorize mental illness. WOADL-BDDC is put through its paces using ADNI 3D. Compared to state-of-the-art classification results from Vgg16, SVM, ResNet50-RF, the suggested technique achieved the greatest accuracy. It was demonstrated that the suggested model is superior to other models for classification from MRI images. In simulations, the proposed approach is shown to be effective in optimizing hyperparameters with an accuracy of 94.38 % on TR set and 94.87% on TS set, Precision of 96.43% on TR set and 97.62% on TS set, and an F1-Score of 89.35 % and 92.10% on TR and TS set, respectively.