SH-StNN: prognostication of Alzheimer's disease based on search and hunt-based stacked deep convolutional neural...

The conventional Machine Learning (ML) approaches for Alzheimer's disease (AD) detection using MRI images deployed the complex feature extraction strategies, consumed huge training time, and exhibited poor detection results. Particularly, Convolutional Neural Networks (CNNs) failed to capture long-range correlations from different brain regions, and suffer from overfitting issues. Hence, Select and Hunt Optimized Stacked Deep Convolutional Neural Network (SH-StNN) is proposed that automatically captures the intricate patterns associated with the brain structures, resulting in accurate detection for the effective AD detection. Architecturally, SH-StNN is constructed with the stacked-CNN layers, where RELU activation function is used. In this research, the Select and Hunt Optimization (SHO) algorithm is applied for medical image segmentation and effective classifier training, which optimizes the fifteenth layer of SH-StNN model. The experimental analysis demonstrates that the SH-StNN model shows improved accuracy of 98%, outperforming the existing techniques, such as Deep CNN by 13.17%, and CT-GAN by 10.81% for 80% of the training using the ADNI dataset. Additionally, the proposed SH-StNN model reports the accuracy of 96.73%, sensitivity of 96.90%, and specificity of 96.96% for the OASIS dataset.