Hand-Sketchs based Parkinson's disease Screening using Lightweight Deep-Learning with Two-Fold Training and Fused Optimal Features


  • Venkatesan Rajinikanth Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering
  • Sahar Yassine Department of Applied Data Science, Noroff University College, Norway
  • Syed Ahmad Bukhari Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St John’s University




Parkinson's disease, Hand sketches, Deep learning, MobileNet, Features selection, classification


Older people experience different Age-Associated Diseases (AAD), and appropriate diagnosis and treatment will help them to lead a peaceful life. Parkinson's disease (PD) is one of the ADD generally found in people aged >60 years. The clinical-level screening of PD is performed using different procedures, including the hand sketches, such as wave/spiral patterns. The proposed research implements Pretrained Lightweight Deep-Learning (PLDL) methods with two-fold training to detect the patterns accurately from hand sketches belonging to the healthy/PD class. The developed system consists of the following stages; (i) Image preprocessing, (ii) Data augmentation, (iii) Two-fold training to improve the detection accuracy, and (iv) Deep features selection with 50% drop-out and binary classification. This work considered the fused features of MobileNets to achieve better detection accuracy. The outcome of this research confirms that this procedure offers a satisfactory result in predicting PD from the considered test images. The K-Nearest Neighbor (KNN) classifier offered a detection accuracy of 100% with the chosen database.




How to Cite

Rajinikanth, V., Yassine, S., & Bukhari, S. A. (2023). Hand-Sketchs based Parkinson’s disease Screening using Lightweight Deep-Learning with Two-Fold Training and Fused Optimal Features . International Journal of Mathematics, Statistics, and Computer Science, 2, 9–18. https://doi.org/10.59543/ijmscs.v2i.7821



Original Research Articles