Multi-Phase Parkinson's Disease Detection: An Adaptive Deep Learning Approach Combining Brain MRI Analysis, Gait Dynamics, and Automated Reporting

Parkinson's disease is a progressive neurodegenerative disorder affecting motor functions, leading to difficulties in movement, posture instability, and cognitive impairment. The proposed solution integrates advanced Deep Learning techniques to enhance early detection and monitoring of Parkinson's disease. The system consists of multiple phases, including Brain Magnetic Resonance Imaging (MRI) scan image-based prediction using Deep Convolutional Neural Networks (DCNN), Gait Dynamics Analysis through a 3D-CNN and LSTM Hybrid Model, and Automated Report Generation. The feature index includes CNN - based medical image analysis, key point - based gait assessment, and NLP based report generation. The implemented models achieved high performance, ensuring reliable predictions and aims to improve early diagnosis, assist healthcare professionals, and improve patient monitoring for better disease management.