![]() ![]() ![]() The causes of most cases of PD (known as ‘sporadic’ or ‘idiopathic’ PD) are still unknown, but involve complex interactions between genetic and environmental factors. The SNpc is a compact structure in the midbrain that plays a vital role in motor coordination and movement control by producing a chemical substance called dopamine, which is integral for controlling the initiation, velocity, and fluidity of voluntary movement sequences. Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised histologically by the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of Lewy bodies in various parts of the brain. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting. With \(93.5\%\) accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. ![]()
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