An Ensemble of the Convolutional Neural Network Model with Fuzzy Fusion Rank Algorithm for the Identification of Parkinson’s Disease Using Magnetic Resonance Imaging Images

Abstract

Abstract Background: Parkinson’s disease (PD) is a degenerative condition of the central nervous system primarily affecting the substantia nigra in the brain, resulting in the loss of dopamine-producing neurons and subsequent motor function deterioration. Early symptoms typically include hand tremors followed by bradykinesia, rigidity, postural instability, and decreased balance. Early diagnosis and proactive management are crucial for improving patients’ quality of life. Methods: In this study, an ensemble of deep learning (DL) models was developed to predict PD using DaTscan images. Initially, DL models (VGG16, ResNet50, Inception-V3 were utilized to classify PD in its early stage. Subsequently, to enhance the overall performance of the classification model, an ensemble strategy based on the fuzzy fusion rank algorithm was employed. The Parkinson’s Progression Markers Initiative database served as the evaluation dataset for the proposed model. This may offer some insight into why certain involuntary symptoms of PD occur, such as fatigue, irregular blood pressure, diminished peristalsis, and unexpected decreases in blood pressure. The ensemble model demonstrated superior recognition accuracy, precision, sensitivity, specificity, and F1-score, achieving 98.45%, 98.84%, 98.84%, 97.67%, and 98.84%, respectively. Results: Compared to individual models, the ensemble model demonstrated superior recognition accuracy, precision, sensitivity, specificity, and F1-score, achieving 98.92%, 98.84%, 98.84%, 97.67%, and 98.84%, respectively. In addition, a publicly available graphical user interface (GUI)-based software program was developed, leveraging magnetic resonance imaging for efficient and accurate classification of PD and its subclasses. The method outperformed contemporary techniques in PD detection, offering significant potential for real-time disease identification. Conclusion: The establishment of a GUI-based software application can substantially aid in the timely detection of PD, facilitating proactive management and improving patient outcomes. The study underscores the importance of leveraging advanced DL techniques and ensemble methods for accurate and efficient disease prediction. Moreover, the development of user-friendly software tools holds promise for widespread adoption and enhanced patient care in the field of PD diagnosis and management.