An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning
Rangamati Science and Technology University · Khulna University of Engineering and Technology · +3 more institutions
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of…
Citation impact
- FWCI
- 36.19
- Percentile
- 100%
- References
- 39
Authors
6- TMTanjim MahmudCorresponding
Rangamati Science and Technology University
- KBKoushick BaruaCorresponding
Rangamati Science and Technology University
- SUSultana Umme Habiba
Khulna University of Engineering and Technology
- NSNahed Sharmen
Chattagram Maa-O-Shishu Hospital Medical College
- MSMohammad Shahadat Hossain
University of Chittagong
Topics & keywords
- Interpretability
- Artificial intelligence
- Deep learning
- Computer science
- Machine learning
- Transfer of learning
- Neuroimaging
- Convolutional neural network
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