International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Alzheimer’s disease is a progressive neurodegenerative disorder that leads to irreversible cognitive decline and memory impairment. Early detection of the disease is essential for effective treatment planning and improved patient care. Magnetic Resonance Imaging (MRI) plays a vital role in identifying structural changes in the brain associated with Alzheimer’s disease. Recent advancements in deep learning have enabled automated and accurate analysis of medical images, reducing dependency on manual interpretation.
This paper presents a Deep Learning Framework for Alzheimer’s Disease detection using Brain MRI images. A Convolutional Neural Network (CNN) is developed to classify MRI images into different stages of Alzheimer’s disease after preprocessing techniques such as resizing and normalization. In addition to image-based classification, a supporting clinical data-based prediction module using a Random Forest classifier is integrated to enhance prediction reliability.
The system is implemented as a web-based application that allows users to upload MRI images and clinical data for real-time prediction. Experimental evaluation demonstrates high classification accuracy and reliable performance, highlighting the effectiveness of combining deep learning and machine learning techniques for early Alzheimer’s disease detection. The proposed framework provides a scalable and practical solution suitable for academic research and future healthcare applications.

Keywords: Alzheimer’s Disease, Brain MRI, Deep Learning, Convolutional Neural Network, Random Forest, Medical Image Analysis

Downloads: | DOI: 10.17148/IJARCCE.2026.151121