WASSERSTEIN GAN-GRADIENT PENALTY WITH DEEP TRANSFER LEARNING FOR 3D MRI ALZHEIMER DISEASE CLASSIFICATION

Abstract

MRI scans for Alzheimer's disease (AD) detection are popular. Recent computer vision (CV) and deep learning (DL) models help construct effective computer assisted diagnosis (CAD) models for AD detection and categorization. Due to their dependence on huge training datasets and effective hyper parameter tuning procedures, most models failed. Transfer learning adjusts the final fully linked layers to use trained DL models on smaller datasets. It handles picture categorization problems well. This research introduces a Wasserstein GAN-Gradient Penalty with Deep Transfer Learning (WGANGP-DTL)-based AD classification model on 3D MRI data. WGANGP-DTL aims to accurately identify and classify AD. WGANGP increases dataset size. The WGANGP-DTL model pre-processes MRI images using image enhancement and segments them using 3DSFCM. Feature extraction uses ant lion optimizer (ALO) with Inception v3 model. For classifying AD, deep belief network (DBN) model is used. The WGANGP-DTL model is experimentally validated using benchmark 3D MRI datasets. WGANGP-DTL outperformed recent methods in experiments.