Ηyper-sinh-Convolutional Neural Network for Early Detection of Parkinson’s Disease from Spiral Drawings

Abstract

Modern developments in the state-of-the-art open-source activation functions for Convolutional Neural Networks (CNNs) have broadened the selection of benchmark activations for Deep Learning (DL)-aided classification. Nevertheless, achieving discrimination of non-linear input image data in CNN is still not straightforward and it is unclear how such novel activation functions can have translational applications with tangible impact. hyper-sinh, made freely available in TensorFlow and Keras, was demonstrated as a benchmark activation function on five (N=5) datasets in its ground-breaking paper. Measuring the value from deploying this activation in a specific application is pivotal to supply the required evidence of its performance on real-life supervised DL-based image classification tasks. In this study, a CNN was for the first time combined with hypersinh to aid early detection of Parkinson’s Disease (PD) from discriminating pathophysiological patterns extracted from spiral drawings. Thus, the hyper-sinh activation was deployed to maximise the separability of the input features from spiral drawings via automated pattern recognition. We demonstrate the accuracy and reliability of hyper-sinh-CNN to aid early diagnosis of PD, evaluated against other gold standard activation functions, including the recent Quantum ReLU (QReLU) and the modified Quantum ReLU (m-QReLU) that solved the ‘dying ReLU’ problem for the first time in the literature of DL. Two (N=2) benchmark datasets from the database of the Botucatu Medical School, São Paulo State University in Brazil, scaled to be in 28 by 28 pixels as the MNIST benchmark data, were used to discriminate between input image patterns of 158 subjects (53 healthy controls and 105 patients with PD) from spirals drawn on graphics tablets. Overtraining was avoided via early stopping and the models were developed and tested in TensorFlow and Keras (Python 3.6). The supervised model (hyper-sinh-CNN) could detect early Parkinson’s Disease with 81% and 91% classification accuracy from the two datasets respectively (F1-scores: 73% and 91% correspondingly). Furthermore, the model achieved high sensitivity (81% and 91%). Thus, this study validates the application of hyper-sinh to aid real-life supervised DL-based image classification, in particular early diagnosis of PD from spiral drawings.