Quantum-enhanced deep learning for Parkinson’s disease classification
4 affiliations
- 1Department of Electronics & Communication Engineering, V R Siddhartha School of Engineering, Siddhartha Academy of Higher Education, Deemed to be University
- 2Manipal Institute of Technology, Manipal Academy of Higher Education
- 3Department of Speech, Language, and Hearing, Callier Center for Communication Disorders, School of Behavioral and Brain Sciences, The University of Texas at Dallas
- 4Department of Conservative Dentistry and Endodontics, Drs S&NR Siddhartha Institute of Dental Sciences
Abstract
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.
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BibTeX
@article{samitha2026quantumenhanced,
title = {{Quantum-enhanced deep learning for Parkinson’s disease classification}},
author = {Kagitha Samitha and Venkata Ratna Prabha K and G. Pradeep Reddy and Radha Kodali and Ramesh Penumaka and Bindu Priya Makala},
journal = {Scientific Reports},
year = {2026},
month = sep,
doi = {10.1038/s41598-026-69594-0},
url = {https://doi.org/10.1038/s41598-026-69594-0},
note = {CC BY},
}
RIS
TY - JOUR
TI - Quantum-enhanced deep learning for Parkinson’s disease classification
AU - Kagitha Samitha
AU - Venkata Ratna Prabha K
AU - G. Pradeep Reddy
AU - Radha Kodali
AU - Ramesh Penumaka
AU - Bindu Priya Makala
PY - 2026
DA - 2026/09/01
JO - Scientific Reports
DO - 10.1038/s41598-026-69594-0
UR - https://doi.org/10.1038/s41598-026-69594-0
AB - 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.
SN - 2045-2322
ER -