Abstract
Parkinson's disease (PD) is a neurodegenerative condition that progressively affects motor function and causes tremors, rigidity, and bradykinesia. Detection of PD at an early stage is important to ensure timely intervention and better patient outcomes. This study uses deep learning algorithms to classify spiral images traced by patients as an inexpensive diagnostic technique for the detection of PD. A database consists of spiral images drawn manually by PD patients and normal individuals, divided into training and testing sets. To discriminate between spiral drawings of Parkinsonian and healthy cases four Convolutional Neural Network (CNN) architecture like DenseNet121, InceptionV3, VGG16, and LeNet are used. Followed by transfer learning which is employed to improve model performance by extracting fine motor impairment patterns in the spirals. DenseNet121 and InceptionV3 achieve competitive performance with 98.44 % accuracy, whereas VGG16 demonstrates excellent feature extraction performance. The study emphasizes the relevance of deep learning in non-invasive PD diagnosis, as a consistent, efficient, and automated method of early detection. The future can be directed towards the combination of spiral images with other biomarkers or a broader data set with other motor measures in a wider disease assessment.•The study focuses on enhancing features extraction by leveraging hybrid deep learning models, improving classification performance.•Implementation of features scaling leads to better model performance, with improved accuracy.•The comparative analysis of CNN architecture provides valuable insights into balancing computational efficiency and classification performance.