Enhancing Parkinson’s Disease Diagnosis: A Deep Learning Framework for Image-Based Detection

Parkinson’s Disease (PD) is a chronic neurological illness that affects millions of people globally and is marked by the gradual degeneration of motor and cognitive functions. Early identification of PD is essential for prompt intervention and better patient outcomes. Conventional diagnostic techniques, often reliant on clinical symptoms, may delay accurate diagnosis, particularly in the early stages of the disease. This paper establishes an innovative approach based on a deep learning framework, specifically the YOLOv8 (You Only Look Once) algorithm, to facilitate the early diagnosis of PD by analyzing MRI brain images. By leveraging YOLOv8's object detection capabilities, this model is trained on extensively annotated medical imaging datasets, focusing on identifying subtle biomarkers associated with PD. The proposed model offers a non-invasive, automated diagnostic tool that surpasses the performance of traditional and existing AI-based models. The proposed model achieved an impressive accuracy of 98%, with a precision of 97% and a recall of 96%, highlighting its robust detection capabilities. Additionally, the model’s ability to process and analyze MRI images in real time provides clinicians with an efficient diagnostic aid, potentially integrating seamlessly into routine clinical workflows. These results depend on the deep learning framework's capacity to improve diagnostic precision, reduce the time required for diagnosis, and contribute to early intervention strategies for PD patients. This approach paves the way for future advancements in AI-driven healthcare and the integration of intelligent diagnostic systems in clinical settings.