Deep Neuro-Fuzzy System For Early-Stage Identification of Parkinson’s Disease Using SPECT Images - S, Jothi; S, Jothi; Sebasthiyar, Anita; S, Sivakumar; S, Sivakumar (Informatica, 2025)
A neurodegenerative disorder called Parkinson’s disease (PD) is identified at the increasing loss of neurons that produce dopamine in the substantia nigra region of human brain. It significantly impairs motor and non-motor functions, thereby diminishing the overall quality of life in affected individuals. A novel framework is proposed for detecting early stage of PD, employing Deep Neuro-Fuzzy System (DNFS) optimized with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Data utilized for this analysis are extracted from 16 image slices showing striatal uptake content in the striatum, named as volume-containing DaTscan image slices (VCDIS) taken from the database called Parkinson’s Progression Markers Initiative (PPMI). The shape and texture characteristics of segmented VCDIS are utilized as features which are combined with Striatal biding ratio (SBR) to distinguish Healthy Individuals (HI) from early-stage PD (EPD). The dataset includes values of 620 DaTscan images with SBR values: 430 from EPD cases and 190 from HI. The effectiveness of the framework is evaluated using 70:30 and 80:20 split ratios, based on metrics such as accuracy, loss, F1 score, precision, and recall. The DNFS-PSO model is presented an impressive accuracy of 98.77% and an error rate of 0.0199 for the chosen features using a 70:30 data split. The outcomes of the proposed model potentially aid clinicians in prompt diagnosis.