Early Detection of Parkinson's Disease Using Deep Learning-Based Voice Signal Analysis

Parkinson’s Disease (PD) is one of the two most widespread neurodegenerative disease impacting a large number of humans globally. Early diagnosis is important as timely medical intervention can significantly improve the quality of life of patients and delay the disease progression. It is defined by the progressive loss of dopamine-producing neurons in the brain, leading to motor symptoms such as tremors, rigidity, slowness of movement, and postural instability. However, conventional diagnostic methods are primarily clinical and neurological examinations, which generally identify the disease after significant neuronal damage has occurred. Artificial Intelligence (AI) and Deep Learning models may resolve the problem by using non-invasive disease diagnosis based on biomedical signals.So my investigation go with a identification of suitable  framework that  integrates speech pre-processing, acoustic feature extraction and deep neural network models to provide accurate, robust and cost effective diagnosis. This idea aims to help healthcare professionals with early screening and to reduce reliance on expensive clinical tests.