Deep machine learning methods for Parkinson's disease diagnosis: a new direction in decision-making systems.

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily characterized by the gradual loss of dopamine-producing neurons in the brain's substantia nigra. The hallmark motor symptoms of PD include tremors, bradykinesia (slowness of movement), rigidity (muscle stiffness) and postural instability. However, the disease also manifests with significant non-motor symptoms, such as cognitive decline, mood disorders, sleep disturbances and autonomic dysfunction, which further complicate the clinical images. Accurate and early diagnosis of PD is challenging due to the subtlety and gradual onset of symptoms, as well as the overlap with other neurodegenerative disorders. Traditional diagnostic methods rely heavily on clinical evaluations and motor symptom assessments, which can be subjective and not detect early or asymptomatic stages of the disease. To overcome these challenges, this work aims to propose a novel Feature-level fusion-enabled PD detection (FLF-PDD) system, integrating an improved bidirectional- gated recurrent unit (Bi-GRU) architecture. This model progresses through several stages: preprocessing, where an Enhanced Gaussian Filtering technique reduces image noise; feature extraction, employing methods, such as Enhanced Pyramid Histograms of Oriented Gradients (PHOG), Multi Texton, LGXP and color analysis; FLF, utilizing principal component analysis (PCA) and tanh normalization to combine extracted features from various MRI orientations; and disease detection, facilitated by the trained Improved Bi-GRU model on fused features to accurately diagnose PD symptoms. The FLF-PDD model undergoes rigorous evaluation to enhance diagnostic accuracy and deepen the understanding of PD progression.