Multi-Model Fusion of CNNs for Identification of Parkinson’s Disease Using Handwritten Samples

When approximately seventy percent of dopamine-producing nerve cells cease to function normally, the symptoms of Parkinson's disease (PD) manifest, marking an irreversible decline in nerve cell health. In clinical settings, neurologists assess individuals by observing their performance in carrying out certain tasks, including writing, drawing, walking, speaking, and assessing facial expressions for any difficulties. This paper focuses on the problem of early PD identification through handwriting and drawing tasks, and by using three well-known PD data-sets. Given the scarcity of handwriting samples and the wide spectrum of Parkinson's disease symptoms, the challenge is known to be particularly difficult. To achieve reliable PD detection, we employ diverse data augmentation techniques to expand the dataset size. Then, we deploy and train the different architectures of deep Convolutional Neural Network (CNN) each of which extract different salient features and aspect of input data due to its unique layout and structure (i.e., number of layers, kernels, normalization, number of connected layers, etc.). After experimental analysis of the performance of individual CNNs, we selected the promising feature vectors and employed different early fusion strategies before final classification. This is a very useful technique that allows a classification model to learn and detect from various representations of data provided by multiple CNNs and improve the overall system performance. Experimental results show that the fusion of freeze features of multiple deep CNN models significantly achieves better exactness of 99.35% in comparison to uni model CNN and other state-of-the-art work.