Parkinson’s disease (PD) involves a slowly advancing neurological condition that profoundly impairs motor as well as cognitive abilities, making prompt and precise diagnosis essential for successful treatment. Diagnosing PD using MRI images is difficult because of subtle changes within the brain and imbalanced datasets. While existing methods such as traditional Machine Learning (ML) and Deep Learning (DL) have made progress, they often struggle to capture important features and effectively handle limited data .
To resolve these concerns, this work suggests a combined approach starting with generating additional MRI images using a Deep Convolutional Generative Adversarial Network (DCGAN) to balance the dataset. The feature extraction from these images is performed using the InceptionV3 model. These features are then enhanced by a Pyramid Attention Network (PAN), which helps to concentrate on the data’s most pertinent sections.
Finally, the enhanced features are classified using a Variational Quantum Classifier (VQC) with amplitude encoding, which leverages quantum-inspired ML techniques to improve classification performance. This pipeline achieved an overall accuracy of 82.04%, outperforming earlier models. The results demonstrate the feasibility of integrating quantum-inspired models within a hybrid framework for PD classification from MRI scans.
Deep convolutional generative adversarial network Gradient-weighted class activation mapping Neuroimaging Informatics Technology Initiative Parkinson’s Integrative Diagnostic Gated Network Parkinson’s precision medicine initiative PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including AbbVie, Alamar Biosciences, Aligning Science Across Parkinson’s, Arrowhead Pharma, Arvinas, AskBio, BIAL, BioArctic, Biohaven, BlueRock Therapeutics, Bristol Myers Squibb, Calico Labs, Capsida Biotherapeutics, Critical Path Institute, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE Healthcare, Genentech, GSK, Insitro, Johnson & Johnson Innovative Medicine, Lundbeck, Merck, Neumora, Neuron23, Novartis, Olink, Regeneron, Roche, Sanofi, Tenvie, UCB, Vanqua Bio, Voyager Therapeutics, The Weston Family Foundation.
Open access funding provided by Manipal Academy of Higher Education, Manipal Department of Electronics & Communication Engineering, V R Siddhartha School of Engineering, Siddhartha Academy of Higher Education, Deemed to be University, Kanuru, Vijayawada, Andhra Pradesh, 520007, India Kagitha Samitha, Venkata Ratna Prabha K & Bindu Priya Makala Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Department of Speech, Language, and Hearing, Callier Center for Communication Disorders, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA Department of Conservative Dentistry and Endodontics, Drs S&NR Siddhartha Institute of Dental Sciences, Chinnoutpalli, India The authors declare no competing interests. This article does not contain any studies with human participants or animals performed by any of the authors. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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