Alzheimer’s Disease Prediction Using Deep Learning

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of
people worldwide. Early diagnosis is crucial for effective intervention and management of the
disease. In recent years, deep learning techniques, particularly Convolutional Neural Networks
(CNNs), have shown promise in medical image analysis for detecting AD. This study explores the
application of two prominent CNN architectures, ResNet and Alex Net, for the prediction of
Alzheimer's disease from brain imaging data, such as MRI and PET scans. The models are pretrained
on large image datasets and fine-tuned on Alzheimer’s-specific medical images. ResNet,
with its deep architecture and residual connections, is used to capture complex features from brain
images, while Alex Net, with a simpler architecture, provides a more computationally efficient
alternative. The models are trained and evaluated using metrics like accuracy, precision, recall,
and AUC-ROC, and the results demonstrate the effectiveness of both models in distinguishing
between Alzheimer’s patients and healthy controls. The study highlights the potential of deep
learning models, particularly ResNet and Alex Net, in improving the accuracy and efficiency of
early Alzheimer’s detection, contributing to advancements in diagnostic methodologies.