YOLO-based ROI selection for joint encryption and compression of medical images with reconstruction through super-resolution network

• Published in: Future generation computer systems Vol. 150; pp. 1 - 9
• Main Authors: Priyanka, Baranwal, Naman, Singh, Kedar Nath, Singh, Amit Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2024
• Subjects: Chaotic map; Huffman code; Medical images; Region of interest; Super-resolution network; YOLOv7; Medical images; Super-resolution network; Chaotic map; Region of interest; YOLOv7; Huffman code
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2023.08.018

Medical images contain significant patient information, and this confidential data should not be accessed without proper authorisation. Concurrently, due to the high redundancy of image data, compression is necessary to minimise image size and efficiently utilise network resources. This paper presents an effective joint encryption and compression method for medical images that prevent critical data leakage while reducing redundancy. Initially, a powerful real-time object detection method, You Only Look Once v7, is employed to accurately and swiftly detect the region of interest (ROI) within the medical images. Subsequently, a joint three-dimensional chaotic map and Huffman encoding are applied to secure medical images without compromising the compression ratio or increasing the time cost. Lastly, a super-resolution network is established at the receiver end to better reconstruct the ROI image for precise diagnostic purposes. The comprehensive experimental analysis demonstrates that our method delivers high levels of security, compression, and visual quality performance on standard datasets used in smart healthcare applications, at a minimum. Furthermore, our approach outperforms other competitive state-of-the-art schemes when compared. We hope this study will inspire further research within the healthcare community. •A joint encryption–compression based method for medical images is proposed.•YOLOv7 is used to detect ROI in medical images with high accuracy and speed.•A reconstruction network is utilized to recover the ROI at receiver side.•Our method outperforms other competitive schemes when compared.