Leveraging Brain MRI for Biomedical Alzheimer’s Disease Diagnosis Using Enhanced Manta Ray Foraging Optimization Based Deep Learning

Abstract

Alzheimer’s disease (AD) is the most frequent method of dementia and ranks the fifth-leading disease. Brain imaging or neuroimaging like magnetic resonance imaging (MRI) was utilized in the medicinal analysis of brain condition to allow visualization of infrastructure and brain functionality. Machine learning (ML) approaches on MRI are utilized in the analysis of AD to accelerate the analysis procedure and assist doctors. But, in typical ML approaches utilizing handcrafted extracting feature systems on MRI are difficult, and requires the contribution of expert users. So, executing deep learning (DL) as an automatic extracting feature system is minimizing require for extracting features and automate the procedure. This article introduces a novel Biomedical Alzheimer’s Disease Diagnosis using Enhanced Manta ray Foraging Optimization based Deep Learning (ADD-EMRFODL) technique on brain MRI. The presented ADD-EMRFODL technique exploits Gabor filtering (GF) technique as a pre-processing step. Also, the presented ADD-EMRFODL technique utilizes densely connected network (DenseNet-121) model to derive feature vectors. Finally, the ADD process is performed via EMRFO with back propagation neural network (BPNN) classifier. The parameter tuning of the BPNN occurs utilizing the EMRFO algorithm with the intention of enhanced classification accuracy. To depict the improvised ADD performance of the ADD-EMRFODL methodology, a comprehensive set of simulations was effectuated. The results stated the improved outcomes of the ADD-EMRFODL algorithm over other current methodologies.

Keywords

• Brain MRI
• deep learning
• neuroimaging data
• biomedical imaging
• metaheuristics
• Alzheimer’s disease