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Open Access

ARTICLE

Secure Medical Image Retrieval Based on Multi-Attention Mechanism and Triplet Deep Hashing

Shaozheng Zhang, Qiuyu Zhang*, Jiahui Tang, Ruihua Xu
School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730000, China
* Corresponding Author: Qiuyu Zhang. Email: email
(This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057269

Received 13 August 2024; Accepted 01 November 2024; Published online 29 November 2024

Abstract

Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.

Keywords

Secure medical image retrieval; multi-attention mechanism; triplet deep hashing; image enhancement; lightweight image encryption

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