Automated Alzheimer's Disease Diagnosis Using Convolutional Neural Networks and Magnetic Resonance Imaging

Alzheimer's disease is a debilitating neurological condition impacting millions globally. Therefore, an accurate diagnosis plays a major role in treating or managing it effectively. Convolutional neural networks (CNNs), popular deep learning algorithms applied to image processing tasks, offer such an opportunity in this study where a CNN model for classifying Alzheimer's patients was proposed. The research yielded impressive results: recall and precision scores as high as 0.9958 which indicate trustworthy identification of true positives while maintaining few false positives; test accuracy exceeding 99% confirming desirable generalization capabilities from the training dataset to live scenarios; ROC AUC score at an astronomical height of 0.9999 signifying great potential in distinguishing between afflicted individuals from their non-affected counterparts accurately. The proposed network achieved a good classification accuracy. Comparing the proposed model alongside two state of the art models VGG19 and ResNet50 resulted in superior performance for the proposed model demonstrating CNNs can increase diagnostic precision and effectiveness of Alzheimer's disease, leading to early detection, enhanced treatment plans, and enriching the quality of life for those affected.