International Journal of Scientific Research and Engineering Development

THIESIS: ”MRI-Based Early Alzheimer’s Stage Classification Using 3D Convolutional Neural Networks and Transfer Learning” Alzheimer’s disease is a progressive neurodegenerative disor- der that mainly features a continuous decline in memory and cognitive functions. The diagnosis given early and accurately is important for effective treatment planning of the disease and to improve the overall patient outcome. MRI is widely used in clinical practice to identify structural changes in the brain, which are associated with Alzheimer’s disease. However, manual interpretation of MRI scans is a time-consuming task and prone to inter-observer variability. This work presents an automated framework for the early- stage classification of Alzheimer’s disease using 3D Convolutional Neural Networks and transfer learning techniques. The proposed model analyzes volumetric MRI brain images for the extraction of spatial and structural features that distinguish multiple stages of the disease. Transfer learning is applied to leverage pre-trained deep learning models, which enhances the performance of the classification with reduced training complexity. The proposed system is designed to assist clinicians by of- fering accurate and early diagnostic support, enhancing clinical decisionmaking, and facilitating timely intervention in the man- agement of AD.

Y.Prasanthi, G.Gayathri, K.Himabindu, A.Joshmika, S.K.Sara Anjum,"MRI-Based Early Alzheimer’s Stage Classification Using 3D Convolutional Neural Network and Transfer Learning" International Journal of Scientific Research and Engineering Development, V9(2): Page(976-979) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.