This study applies a modified EfficientNet deep-learning model with reinforcement-learning-based optimization to classify Parkinson's disease versus healthy controls from MRI scans, reporting very high accuracy (~98%) and strong precision/recall metrics.

While the method could enhance diagnostic decision-support and early detection, it offers minimal mechanistic, biomarker, or therapeutic actionable insight for Parkinson's drug discovery or translational intervention development.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, characterized by the gradual deterioration of dopamine-producing neurons. The main challenge of diagnosing this disease is that physical changes in the brain begin before the patient shows outward symptoms. This leads to the necessity of developing early methods for the detection of this disease. Therefore, with the proposed model, we aimed to significantly improve the early classification of PD using MRI scans by capitalizing on advanced Artificial Intelligence (AI) and Deep Learning (DL) techniques. Our goal of the proposed model was to develop a robust medical decision-support system that enhances the diagnostic precision and supports prompt clinical intervention strategies. The method proposed was a modified EfficientNet DL model combined with the reinforcement learning optimization. This approach enabled a dynamic adjustment of model parameters to effectively minimize the misclassification rates while differentiating MRI scans of PD patients and healthy individuals. Certain performance metrics were used to calculate the performance of the proposed detection model. The results showed that the research achieved high precision, recall, and F1-score values with 98% accuracy for both classes. In the patients (class 0), the precision rate was 95%, the recall rate was 96%, and the F1-score was 98%. Similarly, for healthy individuals (class 1), the precision rate was 93%, the recall rate was 97%, and the F1-Score was 96%. Thus, the proposed EfficientNet model revealed significant enhancements in the diagnostic performance compared to the standard methods. The innovations outlined in this study emphasize the transformative power of AI in enhancing diagnostic predictions. Moreover, the convergence of a DL based EfficientNet model and reinforcement learning based metaheuristic optimization establishes a prospective implementation of predictive analytics in managing high-risk PD with the defined objective of optimizing the patient outcomes within the field of neurology.