Virtual Interface to Identify Alzheimer's Disease Using Deep Learning Techniques

Intending to revolutionize medical diagnosis, this paper attempts to harness the power of deep learning techniques to create a virtual interface to identify Alzheimer's disease. The major public health issue is Alzheimer's disease, a progressive neurological illness. Although it is still a challenging procedure, early identification is crucial to managing the course of the disease. To fulfill this challenge, this work is crucial. Early detection is essential to manage disease progression but remains a difficult task. A Kaggle dataset was used to design the virtual interface for Alzheimer's disease identification. The dataset consists of MRI scan images. This model is a multi-label classifier and uses four convolution layers with dropout layers to discriminate between Alzheimer's disease stages while preventing overfitting. This model is significant since it achieves an amazing validation accuracy of up to 97%. This level of accuracy highlights deep learning's potential as a tool to aid in the early detection of Alzheimer's.