Open Access

ARTICLE

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Seonjae Kim, Dongsan Jun^*
Department of Convergence IT Engineering, Kyungnam University, Changwon, 51767, Korea
* Corresponding Author: Dongsan Jun. Email:
(This article belongs to this Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)

Computers, Materials & Continua 2022, 70(2), 3267-3279. https://doi.org/10.32604/cmc.2022.020651

Received 01 June 2021; Accepted 03 July 2021; Issue published 27 September 2021

Abstract

Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the proposed network architectures, a variety of verification tests were conducted using validation dataset. We used Computer Vision Center-Clinic Database (CVC-ClinicDB) consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration. The proposed MSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNN and DCSC, respectively.

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Keywords

Medical image processing; convolutional neural network; deep learning; telemedicine; artifact reduction; image restoration

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