Hybrid Quantum-Classical Machine Learning for Medical Image Classification under Noisy Conditions
Fuente:
Zenodo
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901506831155200 |
|---|---|
| author | KUNDARAPU, SAI CHANDU |
| author_facet | KUNDARAPU, SAI CHANDU |
| contents | <p>This research presents a hybrid quantum-classical machine learning framework for medical image classification under noisy conditions using chest X-ray images. A classical Convolutional Neural Network (CNN) model and a hybrid quantum-classical model integrating variational quantum circuits were implemented using PyTorch and PennyLane. Gaussian noise was introduced during validation to evaluate model robustness under image perturbations. Experimental results demonstrated comparable performance between the classical CNN and the hybrid quantum-classical model, highlighting the potential of Quantum Machine Learning (QML) for future healthcare AI applications.</p> <p>GitHub Repository:<br>https://github.com/saichandukundarapu/quantum-covid-detection</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20125586 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid Quantum-Classical Machine Learning for Medical Image Classification under Noisy Conditions KUNDARAPU, SAI CHANDU Quantum Machine Learning Healthcare AI Medical Image Classification Chest X-ray Analysis Deep Learning Hybrid Quantum-Classical Learning <p>This research presents a hybrid quantum-classical machine learning framework for medical image classification under noisy conditions using chest X-ray images. A classical Convolutional Neural Network (CNN) model and a hybrid quantum-classical model integrating variational quantum circuits were implemented using PyTorch and PennyLane. Gaussian noise was introduced during validation to evaluate model robustness under image perturbations. Experimental results demonstrated comparable performance between the classical CNN and the hybrid quantum-classical model, highlighting the potential of Quantum Machine Learning (QML) for future healthcare AI applications.</p> <p>GitHub Repository:<br>https://github.com/saichandukundarapu/quantum-covid-detection</p> |
| title | Hybrid Quantum-Classical Machine Learning for Medical Image Classification under Noisy Conditions |
| topic | Quantum Machine Learning Healthcare AI Medical Image Classification Chest X-ray Analysis Deep Learning Hybrid Quantum-Classical Learning |
| url | https://doi.org/10.5281/zenodo.20125586 |