Automated Alzheimer’s Disease Diagnosis using Convolutional Neural Networks and Magnetic Resonance Imaging

Abstract

Alzheimer’s disease is a debilitating neuro-logical condition affecting millions globally; therefore, correct diagnosis plays a significant role in treating or managing it effectively. Convolutional neural networks (CNNs), which are popular deep learning algorithms are applied to image processing tasks, offer a good technique to study and investigate images processing. In this study, a CNN model for classifying Alzheimer’s patients is 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 classification accuracy of 99.94% on LMCI vs EMCI, 99.87% on LMCI vs MCI, 99.95% on LMCI vs AD, 99.94% on LMCI vs CN, 99.99% on CN vs AD, 99.99% on CN vs EMCI, 99.99% on CN vs MCI, 99.99% on AD vs EMCI, 99.98% on AD vs MCI, and 99.96% on MCI vs EMCI. The proposed CNNs model is compared with two ultramodern models such as VGG19 and ResNet50. The results show that the proposed model achieved a superior performance in diagnostic precision and effectiveness of Alzheimer’s disease, leading to early detection, enhanced treatment plans, and enriching the quality of life for those affected.