Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection

• Published in: International journal of reconfigurable and embedded systems Vol. 13; no. 1; p. 179
• Main Authors: Sharma, Manoj Kumar, Kaiser, M Shamim, Ray, Kanad
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2024
• Subjects: Algorithms; Alzheimer's disease; Artificial neural networks; Diagnosis; Lightweight; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Optimization techniques; Particle swarm optimization; Performance enhancement; Performance measurement; Positron emission; Weight reduction
• ISSN: 2089-4864; 2722-2608; 2089-4864
• DOI: 10.11591/ijres.v13.i1.pp179-191

The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.