The increasing prevalence of neurodegenerative diseases such as Alzheimer's and Parkinson's has led to a growing demand for advanced diagnostic techniques. This project, Revolutionizing Neurodegenerative Disease Treatment with Deep Learning Models, aims to enhance early detection and classification of these diseases through deep learning algorithms. Specifically, we employ Convolutional Neural Networks (CNN), Mobile Net-SVM in hybrid mode, and DenseNet models to accurately distinguish between normal, Alzheimer's, and Parkinson's conditions based on medical data.

The project workflow encompasses several key modules: data collection, pre-processing, and splitting, followed by model building and evaluation. The deep learning models, CNN, DenseNet, and Mobile Net-SVM, are trained to identify patterns in the dataset and predict the disease state. The system is designed to be user-friendly, providing modules for user registration, login, data input, and result retrieval, followed by an option for users to logout.

The ultimate goal of this project is to provide a powerful tool for the healthcare industry to assist in the early diagnosis and personalized treatment of neurodegenerative diseases, leveraging the capabilities of state-of-the-art deep learning techniques for better accuracy and efficiency in medical predictions.

Keyword: neurodegenerative diseases, deep learning models, Convolutional Neural Networks (CNN), MobileNet-SVM, DenseNet, Alzheimer's, Parkinson's, disease classification, data preprocessing, model evaluation, early detection, healthcare,