Integrating Imaging and Clinical Data for Parkinson's Disease Diagnosis using Deep Learning

That disorder affecting the nervous system, Parkinson's disorder can worsen with time and cause a host of health problems. Parkinson's disease diminishes cerebral function and may induce physical complications in senior individuals. Parkinson's disease can induce a range of disorders by impairing neurological in nature, behavioral, and physiological systems. Physical structures exhibit minimal damage at early diagnosis, complicating the prediction of Parkinson's illness in its initial stages based solely on physical observation. Recent studies on electroencephalogram (EEG) signs in the identification of Parkinson's illness have revealed significant aspects pertinent to early detection. Electroencephalography (EEG) is a multifaceted signal comprising several non-linear and non-stationary elements. Subsequently, an auto-encoder-based deep extraction of features method is employed to derive the spatial and temporal attributes from the four frequency partitions. Additionally, statistical and spectral attributes are obtained. The gathered attributes are utilized to analyze notable patterns associated with Parkinson's disease. Optimal weighted feature fusion is achieved through the precise adjustment of weights. The proposed DLB-LSTM (Densely Located Bidirectional Long Short Term Memory) employs weighted features so that we can identify PD. To impede the progression of Parkinson's disease, the algorithm autonomously learns from the signals and delivers targeted outcomes.