A Quantum-Enhanced Artificial Neural Network Model for Efficient Medical Image Compression
quantum feature extraction; Quantum machine learning; quantum multiclass classifier; supervised learning
The ability to effectively store and transmit high-resolution images such as MRI and CT scans without losing quality is critical to modernizing medical imaging. Traditional compression methods risk losing essential medical image data, which requires perfect detail for diagnosis. Quantum algorithms use superposition and entanglement to compress faster while preserving important information. This research presents a Quantum-enhanced Artificial Neural Network (QANN) model that combines quantum feature extraction with classical neural network topologies to improve image compression. Our approach consists of converting standardized classical data into quantum states, controlling these states using parameterized quantum circuits, and measuring the resulting states to produce enhanced feature vectors. The quantum-enhanced features are fed into a traditional neural network for image compression. The experimental results clearly show that our QANN framework outperforms standard models in terms of accurate reconstructed images, reduced size, and increased space-saving percentage, especially when dealing with large and complicated datasets. The QANN model demonstrates how quantum computing can significantly enhance the effectiveness of medical image processing solutions. Kaggle brain CT and MRI datasets and COVID-CXNet chest x-ray images are used. The proposed QANN model improves peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Using quantum technology, the image size is reduced for MRI (73.3 %), X-ray (74.1%), and CT-SCAN (71.8%) to save space. 2025 IEEE.
Subbiyan, Balasubramani; Prabhavathi Neelakandan, Renjith; Leelasankar, Kavisankar; Rajavel, Rajkumar; Malarvel, Muthukumaran; Shankar, Achyut
Institute of Electrical and Electronics Engineers Inc.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1109/ACCESS.2025.3542807 https://www.scopus.com/pages/publications/85218484108?origin=resultslist
Subbiyan B., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vijayawada, 520002, India; Prabhavathi Neelakandan R., Vellore Institute of Technology, School of Computer Science and Engineering, Chennai, 600127, India; Leelasankar K., SRM Institute of Science and Technology, Faculty of Engineering and Technology, Department of Computing Technologies, Tamil Nadu, Kattankulathur, 603203, India; Rajavel R., CHRIST University, School of Engineering and Technology, Department of Computer Science and Engineering, Karnataka, Bengaluru, 560029, India; Malarvel M., Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation (DU), Department of Computer Science and Engineering, Tamil Nadu, Paiyanur, 603104, India; Shankar A., Bennett University, School of Computer Science Engineering and Technology, Uttar Pradesh, Greater Noida, 201310, India