Parkinson's Diseases and its Severity Prediction using Transfer Learning Techniques

Parkinson's disease (PD) is a disorder that has significant implications, for patient care and treatment. It is crucial to predict the severity of the disease to provide personalized management. This research study explores the use of transfer learning techniques with medical image datasets to make these predictions. Proposed approach involves utilizing learning models that have been trained on large-scale image datasets. By leveraging the knowledge and features learned from these models the severity of Parkinson's disease can be effectively forecast. The study utilizes Convolutional Neural networks (CNNs) to extract important information from medical images, which is then inputted into a predictive model. The results of our experiments demonstrate the effectiveness of transfer learning in predicting PD severity. The proposed study compares trained CNN architectures, such as Simple CNN, Random Forest and XGBoost algorithms, DenseNet, and Inception using a specialized medical image dataset. The resulting model shows accuracy, sensitivity, and specificity making it a valuable tool for assisting physicians in their decision-making processes. Furthermore, the proposed technique not only achieves prediction accuracy but also provides valuable insights into visual indicators of disease progression. This non-invasive and efficient approach has the potential to empower professionals with early diagnosis and personalized treatment strategies for individuals, with Parkinson's disease. This groundbreaking approach shows potential in improving the management of Parkinson's disease and enhancing the quality of life for those who are impacted by it.