International Conference on Computer and Knowledge Engineering

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Early detection of Parkinson’s disease using Convolutional Neural Networks on SPECT images

Authors :

Reyhaneh Dehghan¹ Marjan Naderan² Seyyed Enayatallah Alavi³

1- Shahid Chamran University of Ahvaz 2- Shahid Chamran University of Ahvaz 3- Shahid Chamran University of Ahvaz

Keywords :

Parkinson’s disease،convolutional neural networks،SPECT images،deep learning methods

Abstract :

Parkinson’s Disease or PD, is a neurological disorder that mainly affects dopamine-producing neurons in a specific area of the brain namely, the substantia nigra. Despite the fact that this disease has been known for many years, accurate diagnosis of Parkinson's disease in its early stages still remains a challenge for physicians and researchers. In this study, a convolutional neural network (CNN) is used to diagnose the disease, which is able to differentiate between patients with Parkinson's disease from healthy individuals based on Single-Photon Emission Computed Tomography (SPECT) images. The proposed method consists of four phases: preprocessing, Data Augmentation, training and testing/evaluation. A total of 650 SPECT images were analyzed in this study, which were taken from the Parkinson's Progression Markers Initiative (PPMI) Database. Simulation results compared with other classification methods, show an accuracy of 97.01%, recall of 96.61%, specificity of 96.61%, and an f1-score of 96.61%. In addition, to improve the results, data augmentation is added to the method to increase the number of sample images. Results of adding data augmentation also show an accuracy of 95.50%, recall of 98.88%, specificity of 97.82%, and an f1-score of 98.32%, which are promising compared to previous work.

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