Prediction of Alzheimer’s Disease Using DHO-Based Pretrained CNN Model

Abstract

Detecting Alzheimer’s disease (AD) early on allows patients to take preventative measures before the onset of irreversible brain damage, which is a critical factor in the treatment of Alzheimer’s patients. Most machine detection methods are constrained by congenital observations, although computers have been utilized in several recent research studies to diagnose AD. In AD, the hippocampus is usually the first part of the brain to be affected. Structural magnetic resonance imaging (SMRI) can be used to assist in diagnosing AD by measuring the hippocampus’s form and volume (MRI). The information encoded by these attributes is restricted and may be affected by segmentation problems. These traits are also extracted independently of the classification, which could result in lower-than-desired classification accuracy. Researchers in this study used structural MRI data to develop a deep learning framework for combined automatic hippocampus segmentation and AD categorization. Multi-task deep learning (MTDL) is used to learn hippocampus segmentation simultaneously. The hyperparameter optimization of the CNN model (capsule network) for illness classification is then carried out using the deer hunting optimization (DHO) technique. ADNI-standardized MRI datasets have been used to test the suggested method, and it is accurate. Suggested MTDL achieved 97.1% accuracy and 93.5% of Dice coefficient, whereas the proposed MTDL model achieved an accuracy of 96% for binary classification and 93% for multi-class classification.