Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data

Jigna Hathaliya, Raj Parekh, Nisarg Patel, Rajesh Gupta, Sudeep Tanwar, Fayez Alqahtani, Magdy Elghatwary, Ovidiu Ivanov, Maria Simona Raboaca and Bogdan-Constantin Neagu
Additional contact information
Jigna Hathaliya: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Raj Parekh: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Nisarg Patel: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Rajesh Gupta: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Fayez Alqahtani: Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Magdy Elghatwary: Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Ovidiu Ivanov: Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania
Maria Simona Raboaca: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Valcea, 240050 Râmnicu Vâlcea, Romania
Bogdan-Constantin Neagu: Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania

Mathematics, 2022, vol. 10, issue 15, 1-15

Abstract: In this paper, we used the single-photon emission computerized tomography (SPECT) imaging technique to visualize the deficiency of dopamine-generated patterns inside the brain. These patterns are used to establish a patient’s disease progression, which helps distinguish the patients into different categories. Furthermore, we used a convolutional neural network (CNN) model to classify the patients based on the dopamine level inside the brain. The dataset used throughout this paper is the Parkinson’s progressive markers initiative (PPMI) dataset. The collected dataset was pre-processed and data amplification was performed to balance the imbalanced dataset. A CNN-based neural network was defined to classify input SPECT images into four categories. The motivation behind the proposed model is to reduce the number of resources consumed while maintaining the performance of the classification model. This will help the healthcare ecosystem run the classification model on mobile devices. The proposed model contains 14 layers with input layers, convolutional layers, max-pool layers, flatten layers, and dense layers with different dimensions. The dense layer classifies the patients into four different categories, including PSD, healthy control, scans without evidence of dopaminergic deficit (SWEDD), and GenReg PSD from the entire SPECT imaging dataset, which is used to establish the disease progression of different patients using SPECT images. The proposed model is trained with a large dataset with 58,692 images for training and 11,738 images for validation, and 7826 for testing. The proposed model outperforms the classification models from the surveyed papers. The proposed model’s accuracy is 0.889, recall is 0.9012, the precision is 0.9104, and the F1-score is 0.9057.

Keywords: Parkinson’s disease; SPECT; CNN; classification; dopaminergic neurons; regions of interest (ROI) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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