A hybrid filtering and deep learning approach for early Alzheimer's disease identification.
Authors
Affiliations (7)
Affiliations (7)
- Department of Computer Science and Engineering, Jamalpur Science and Technology University, Jamalpur, Bangladesh.
- Department of Cyber Security Engineering, Gazipur Digital University, Kaliakair, 1750, Bangladesh.
- Department of Computer Science, Virginia Tech, Blacksburg, 24060, USA.
- Faculty of Engineering and Computing, University of Science and Technology, Aden, Yemen. [email protected].
- Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box-2457, Riyadh, 11451, Saudi Arabia. [email protected].
- Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh. [email protected].
- School of Information Technology, Crown Institute of Higher Education, Canberra, Australia. [email protected].
Abstract
Alzheimer's disease is a progressive neurological disorder that profoundly affects cognitive functions and daily activities. Rapid and precise identification is essential for effective intervention and improved patient outcomes. This research introduces an innovative hybrid filtering approach with a deep transfer learning model for detecting Alzheimer's disease utilizing brain imaging data. The hybrid filtering method integrates the Adaptive Non-Local Means filter with a Sharpening filter for image preprocessing. Furthermore, the deep learning model used in this study is constructed on the EfficientNetV2B3 architecture, augmented with additional layers and fine-tuning to guarantee effective classification among four categories: Mild, moderate, very mild, and non-demented. The work employs Grad-CAM++ to enhance interpretability by localizing disease-relevant characteristics in brain images. The experimental assessment, performed on a publicly accessible dataset, illustrates the ability of the model to achieve an accuracy of 99.45%. These findings underscore the capability of sophisticated deep learning methodologies to aid clinicians in accurately identifying Alzheimer's disease.