A Machine And Deep Learning-Powered Platform For Unified Detection Of Neuro Degenerative Contions - Alzheimer's

Alzheimer's disease is a progressive neurodegenerative disorder that affects memory and cognitive functions, making early diagnosis essential for effective treatment. This work proposes a deep learning-based system for the classification of Alzheimer's disease using MRI brain images. A Convolutional Neural Network (CNN) based on the LeNet architecture is employed to automatically extract relevant features from input images and perform multi-class classification. The model is trained on a publicly available Kaggle dataset consisting of approximately 1200 MRI images. The system is designed to classify different stages of Alzheimer's disease, including normal, mild cognitive impairment, and advanced stages. Data preprocessing techniques such as resizing and normalization are applied to improve model performance. The dataset is split into training and testing sets to evaluate the effectiveness of the model. Experimental results show that the proposed model achieves a high accuracy of 99.5%, while maintaining low computational complexity. In addition to the classification model, a web-based application is developed using Django, allowing users to upload MRI images and obtain real-time predictions along with basic treatment suggestions. The system provides fast and reliable results, making it suitable for practical use. The proposed approach demonstrates that a simple CNN architecture can achieve high performance and can effectively support early diagnosis and clinical decision-making.