A novel approach for Parkinson's disease diagnosis using deep learning and Harris Hawks optimization algorithm with handwritten samples

Parkinson's is a progressive neurological disease without a cure; the early symptoms of the disease usually appear slowly and gradually, and early diagnosis of the disease can help control the disease and reduce treatment costs; however, diagnosis of the disease, especially in the early stages or in situations where The patient's clinical symptoms overlap with other diseases, it will be challenging. In this article, we present a novel approach that leverages the potential of deep learning for disease diagnosis based on patients' handwritten samples. Our method employs the Harris Hawks Optimization (HHO) algorithm to optimize the proposed model and achieve optimal performance. In this study, we conducted a comparative analysis to ensure the optimal performance of the HHO algorithm. After 10 iterations, the HHO algorithm converged to a desirable cost function value of 0.0084746, a highly competitive achievement similar to the GWO algorithm and somewhat better than PSO. Also, to have a more accurate criterion to compare and check the performance of the proposed approach, we compared our results with five pre-trained models AlexNet, GoogleNet, MobileNetV2, ResNet18, and ResNet50. Our approach had the best performance among the investigated models, with an accuracy of 94.12%. The network's performance was measured by various metrics, including Precision, Recall, F1-Score, and AUC, which yielded values of 94.1, 94.24, 94.11, and 0.98, respectively. Additionally, through averaging trained models based on circle, meander, and spiral patterns, we achieved an astonishing 100% accuracy. These results unequivocally demonstrate the outstanding performance of the model. The findings underscore the effectiveness of our proposed approach in the accurate and automated diagnosis of Parkinson's disease based on handwritten samples, thereby offering potential advantages for early intervention and treatment.