Enhancing Early Alzheimer's Detection through Convolutional Neural Networks and Long Short-Term Memory Models

Abstract

This research paper explores the potential of leveraging Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models to improve early detection of Alzheimer's disease. Alzheimer's, a progressive neurodegenerative disorder, poses significant challenges in timely and accurate diagnosis, often relying on subjective assessments and costly imaging techniques. Our study focuses on integrating CNNs and LSTM models to develop a robust, non-invasive diagnostic tool capable of analyzing complex neuroimaging data and identifying early markers of Alzheimer's. The proposed model employs CNNs to extract spatial features from brain MRI scans, capturing critical patterns associated with the onset of Alzheimer’s. These features are then fed into LSTM networks, which are adept at modeling temporal sequences, to enhance the predictive accuracy by considering both spatial and temporal brain changes. We trained and tested our model on a dataset comprising MRI scans of patients at various stages of Alzheimer's, achieving significant improvements in early detection rates compared to traditional methods. The results indicate a high degree of accuracy, precision, and recall, showcasing the model's potential in clinical settings. This approach not only offers a cost-effective alternative to current diagnostic practices but also holds promise for integrating into routine screening procedures, facilitating early intervention and potentially slowing disease progression. Future work will explore the model's applicability across diverse populations and its integration with other diagnostic modalities to further refine its predictive capabilities.