An Efficient Deep Learning Method For Early Detection Of Alzheimer’s Disease Using Mobilenet

DOI

10.2991/978-94-6463-858-5_218How to use a DOI?

Keywords

Machine Learning; Deep Neural Networks; MobileNet; VGG19; ResNet; Accuracy

Abstract

A correct prognosis of Alzheimer’s disease is crucial for effective treatment, especially in its early levels, because it enables mitigation of the risk of severe brain damage. While early diagnosis of Alzheimer’s disease is possible, predicting its onset before signs appear remains a challenge. Deep Learning has emerged as a treasured tool for early advert diagnosis. In this study, we employ several deep neural network architectures, including VGG-19, ResNet, and MobileNet, to aid researchers in early disease detection. Alzheimer’s disease is a revolutionary neurodegenerative disorder that critically affects cognitive abilities, reminiscence retention, and everyday sports. Early detection is important for timely medical intervention and enhancing patient quality of life. This research provides a comparative analysis of those fashions, identifying MobileNet as the most efficient due to its lightweight structure and high accuracy. Medical imaging datasets, such as MRI and CT scans, train MobileNet to classify and recognize advertisements in their early stages. The proposed device leverages MobileNet’s computational performance, making it scalable and appropriate for deployment on aid-restrained gadgets such assmartphones and embedded systems. MobileNet achieves 97% accuracy in classifying Alzheimer’s disease stages, demonstrating its efficiency in medical image analysis. The version undergoes giant preprocessing and education to enhance accuracy, reliability, and flexibility. Assessment metrics such as accuracy, precision, and F1-score conclude the performance assessment, showcasing MobileNet’s superiority in Alzheimer’s disease prediction.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bodduru Keerthana
AU  - V. J. Sai Varun
AU  - V. Aswini Lavanya
AU  - M. Jahnavi
AU  - A. S. V. Sai Ram
PY  - 2025
DA  - 2025/11/04
TI  - An Efficient Deep Learning Method For Early Detection Of Alzheimer’s Disease Using Mobilenet
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2628
EP  - 2646
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_218
DO  - 10.2991/978-94-6463-858-5_218
ID  - Keerthana2025
ER  -