• ETHOTI NAVEENA
• Katepogu Surendra
• G. Eswar

Alzheimer's disease (AD) remains a significant challenge in the field of neuroimaging. Structural magnetic resonance imaging (sMRI) has proven to be a valuable tool for identifying the structural changes in the brain associated with AD. However, traditional deep learning models, such as convolutional neural networks (CNNs), often struggle to effectively capture the complex, long-range dependencies across various brain regions, which are crucial for accurate disease detection.An innovative approach to Alzheimer's disease detection that leverages the MobileNet architecture, a lightweight and efficient deep learning model, designed to address the limitations of conventional CNNs. MobileNet's ability to process highdimensional data with fewer parameters makes it well-suited for this application, offering a promising alternative to existing models. In addition to the advanced AI model, the proposed system includes the development of a user-friendly web application that streamlines the healthcare process for both patients and doctors. This platform will allow patients to easily book appointments, facilitating early diagnosis and intervention. Doctors will be able to securely upload sMRI images and prescriptions directly through the platform, enabling comprehensive assessment and personalized treatment planning. The system classifies Alzheimer's disease into four categories: "no dementia," "very mild dementia," "mild dementia," and "moderate dementia." By integrating MobileNet for disease classification with an intuitive web-based interface, the system aims to improve the efficiency and accuracy of Alzheimer's detection, ultimately enhancing patient care.

"Improving Alzheimer’s Detection Accuracy: A MobileNet Implementation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.a335-a342, July-2025, Available :http://www.jetir.org/papers/JETIR2507034.pdf

"Improving Alzheimer’s Detection Accuracy: A MobileNet Implementation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppa335-a342, July-2025, Available at : http://www.jetir.org/papers/JETIR2507034.pdf