AI-Driven Alzheimer’s Detection Performance Analysis of Pretrained CNN Models on MRI Data

Alzheimer's disease (AD) is a memory and cognitive function neurodegenerative disease. Early diagnosis is important for appropriate treatment, but conventional diagnosing techniques are not very effective. In this paper, automatic detection of Alzheimer's disease from MRI scans using deep learning techniques is discussed. We trained and compared six pre-trained Convolutional Neural Network (CNN) models—VGG19, ResNet101, EfficientNetB3, MobileNetV2, InceptionV3, and DenseNet121—to differentiate MRI scans as Alzheimer and Non-Alzheimer. In experiments, InceptionV3 is the optimal model for classification of Alzheimer's disease and all others are inferior with an accuracy of 99.2%. Confusion matrices, classification reports, and accuracy metrics were utilized to test performance. We also created a web-based real-time MRI classification tool with a user-friendly interface for clinicians. This work showcases the potential of deep learning in medical imaging and the detection of early disease, and opens the door to AI-augmented improvement in Alzheimer's diagnosis