Deep Learning-Driven Alzheimer’s Disease Classification: Custom CNN and Pretrained Architectures for Accurate MRI Analysis

Millions of people worldwide suffer from Alzheimer's Disease (AD), a progressive neurological disorder. An early and accurate diagnosis is necessary to treat the disease more effectively. This study investigates the application of deep learning methods in classifying the Alzheimer's disease using medical imaging data. A Custom Convolutional Neural Network (CNN) was developed, and it outperformed the performance of the several state-of-the-art pre-trained models, including DenseNet121, VGG16, InceptionV3, and ResNet50, with an exceptional accuracy of 98.18%. The research demonstrates the potential of deep learning algorithms in improving the medical diagnoses by using both transfer learning techniques and custom designed architectures.