Alzheimer’s Disease Detection Using Multimodal AI

The project focuses on developing a robust Alzheimer’s disease detection system using a multimodal AI approach. Given the devastating impact of Alzheimer’s disease, early and accurate diagnosis is crucial. The proposed system leverages Convolutional Neural Networks (CNNs) with two distinct architectures, VGG16 and ResNet50, to analyze neuroimaging data. By utilizing these two models, the system aims to enhance detection accuracy and provide comprehensive classification reports. The VGG16 model, known for its simplicity and depth, excels in extracting high-level features, while ResNet50, with its residual connections, addresses the vanishing gradient problem, allowing for deeper network training. The combination of these models ensures a balanced approach, capturing both fine-grained and complex features of brain images. The project also includes a thorough literature survey of existing techniques, identifying gaps and challenges in current methodologies. The end goal is to create a system that can aid in the early diagnosis of Alzheimer’s, providing a reliable tool for healthcare professionals.