AUTOMATED DETECTION OF PARKINSON’S DISEASE BASED ON HYBRID CNN AND QUANTUM MACHINE LEARNING TECHNIQUES IN MRI IMAGES

Abstract

Parkinson’s disease (PD) is a long-term neurological condition that causes severe neuronal degeneration in the motor cortex. Because of the constraints of medical applications determining the severity of PD, forecasting its development can be difficult. Recently research has focused on artificial intelligence to automatically diagnose PD from MRI images. The proposed study aims (a) to incorporate automated method for augmentation of the MRI images using the deep convolutional generative adversarial networks (DCGAN), (b) to perform feature extraction and classification by employing the pre-trained models such as VGG16, Xception, and InceptionV3 models, (c) to develop a hybrid model by fusing the InceptionV3 features and classification using QSVM model. A total of 60 real-time MRIs of normal (N = 30) and PD (N = 30) patients were included in this study. To increase the dataset, the proposed study employed the DCGAN approach. Thus, the dataset for this study was increased to 1000 (normal and PD) images. Automatic feature extraction and classification were performed utilizing different pre-trained models such as VGG16, Xception, and InceptionV3. Among the pre-trained models InceptionV3 architecture provided high accuracy of 74% compared to other models. The proposed work developed a hybrid model by fusing InceptionV3 features and a quantum support vector machine (QSVM) for the detection of PD and healthy groups. The proposed model attained a prediction accuracy of 87.5% and high precision of 95%, respectively. Additionally, the hybrid architecture provided high recall and F1-measure of 84% and 89%, respectively. The hybrid model provided lowest false negative and false positive of 4 and 1, respectively. Therefore, the hybrid model could be an effective diagnostic tool for the automated prediction of PD.