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

Abstract

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.