Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection - Mallesh, Muppana Greeshmanth; Venkateswarlu, B (Journal of Neonatal Surgery, 2025)
Parkinson’s disease (PD) diagnosis remains challenging due to the absence of definitive clinical tests, particularly in its early stages. This study addresses the critical need for an effective and non-invasive methodology for early PD detection by leveraging deep learning, specifically Convolutional Neural Networks (CNNs), to analyze handwriting patterns. Various models including ResNet50, VGG19, InceptionV3, and Xception are employed for feature extraction, with K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree used for classification. The proposed ensemble method combines predictions from multiple models, enhancing accuracy. In the base model, ResNet50 + VGG19 + InceptionV3 with KNN achieved 95% accuracy. As an extension, further exploration of ensemble techniques, including Voting Classifier, is conducted, aiming for 98% accuracy or higher. Additionally, a front end using Flask framework is developed for user testing, incorporating user authentication. This research contributes to advancing early PD detection, crucial for prescribing timely treatment and improving patients' quality of life