Improving Alzheimer’s Disease Detection with Transfer Learning

Showkat A.  Dar; Aafaq A.  Rather; Mustafa Ibrahim  Ahmed Araibi; I.  Elbatal; Ehab M.  Almetwally; Ahmed M.  Gemeay; Sharvari R.  Shukla; Faizan  Danish; Qaiser Farooq  Dar

doi:10.6000/1929-6029.2025.14.39

Authors

Showkat A. Dar Department of Computer Science and Engineering, GITAM University Bangalore Campus, India
Aafaq A. Rather Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune, India
Mustafa Ibrahim Ahmed Araibi Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
I. Elbatal Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Ehab M. Almetwally Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Ahmed M. Gemeay Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt
Sharvari R. Shukla Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune, India
Faizan Danish Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology-Andhra Pradesh (VIT- AP) University, Inavolu, Beside AP Secretariat, Amaravati AP-522237, India
Qaiser Farooq Dar Department of Health Research, ICMR-National Institute of Virology Pune, North Zone Jammu-180001, J&K, India

DOI:

https://doi.org/10.6000/1929-6029.2025.14.39

Keywords:

Mobile NetV1, Alzheimer’s Disease, Xception, Transfer Learning, MRI image

Abstract

Accurate and prompt diagnosis of Alzheimer's disease (AD) remains a challenge, with only a small percentage of patients receiving timely confirmation. Manual interpretation of MRI scans, the primary diagnostic tool, is time-consuming, subjective, and prone to error, particularly in differentiating between disease stages. This study aimed to develop a computer-aided diagnosis system (CAD) for AD classification using deep learning models. MobileNetV1 and Xception architectures were employed to classify AD into four stages: mild, normal, moderate, and severe. Transfer learning and layer freezing techniques were applied for feature extraction and classification. Model performance was evaluated using precision, recall score, and accuracy metrics. The Xception model achieved a higher accuracy (79%) compared to MobileNetV1 (73%) in classifying AD stages. Compared to MobileNetV1, this study shows that Xception-based CAD systems have the potential to diagnose AD more accurately, providing a promising path for future research and clinical application.

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