Alzheimer's Disease Detection from Brain MRI Scans Using Convolutional Neural Networks

The rise in Alzheimer's disease cases has become a challenge to global healthcare systems, hence making it mandatory to develop proper accurate diagnostic tools. This paper introduces an aggressive deep learning model using CNNs (Convolutional neural network) to classify Alzheimer's disease into four stages: Non-Demented, Really Gentle Demented, Light Demented, and Reasonable Demented. This variant leverages the well-balanced preparation approach and novel ways of data generation to overcome class imbalance and enhance generalization altogether. The design and practice schedule with monitoring metrics like correctness, AUC, and F 1 rating ensures optimal performance and robustness. The classification ability of the proposed design was observed to be truly remarkable and surpassed conventional tactics both in the dimensions of accuracy and sensitivity. These findings expand the prospects in CNN-based systems directed at the early and accurate diagnosis of Alzheimer's, as they become relevant for medical practice and as a basis for further innovation in medical diagnostics.