Deep Learning for Alzheimer's Disease Classification: CNN Model Using MRI Images

Early discovery of Alzheimer's disease is important for efficient intervention and management since it is a degenerative brain illness that seriously affects cognitive abilities. This work directs to build a CNN model to categorise several phases of Alzheimer's disease depending on MRI data. Drawn from the open-source Kaggle platform, the dataset consists of 6,400 MRI scans separated into four classes. The images were homogeneous pre-processed CNN model training and validation then followed from this. The algorithm showed to be really good in spotting Alzheimer's phases in class with an incredible 99% accuracy. Showing great accuracy, recall, and F1-scores across all categories, the classification report presents a well-balanced and well- performing model. Furthermore, showing low misclassifications in the confusion matrix emphasises the model's capacity to span the several phases of Alzheimer's. The findings show CNN's promise in automated Alzheimer's detection, providing a consistent, non-invasive instrument for early diagnosis and so improving patient care and treatment planning.