Enhancing ultrasound image quality using deep structure of residual network

Ade Iriani Sapitri, Siti Nurmaini, Muhammad Naufal Rachmatullah, Annisa Darmawahyuni, Firdaus Firdaus, Anggun Islami, Bambang Tutuko, Akhiar Wista Arum

Abstract


Ultrasonography, a medical imaging technique, is often affected by various types of noise and low brightness, which can result in low image quality. These drawbacks can significantly impede accurate interpretation and hinder effective medical diagnoses. Therefore, improving image quality is an essential aspect of the field of ultrasound systems. This study aims to enhance the quality of ultrasound images using deep learning (DL). The experiment is conducted using a custom dataset consisting of 2,175 infant heart ultrasound images collected from Indonesian hospitals, and the model is subsequently generalized using other datasets. We propose enhanced deep residual network combined convolutional neural networks (EDR-CNNs) to improve the image quality. After the enhancement process, our model achieved peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM) scores of 38.35 and 0.92 respectively, outperforming other methods. The benchmarking with other ultrasound medical images indicates that our proposed model produces good performance, as evidenced by higher PSNR, lower SSIM, a decrease in mean square error (MSE), and a lower contrast improvement index (CII). In conclusion, this study encapsulates the forthcoming trends in advancing low-illumination image enhancement, along with exploring the prevailing challenges and potential directions for further research.

Keywords


Deep learning; Enhancement; Medical image; Residual network; Ultrasonography

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DOI: http://doi.org/10.11591/ijece.v15i4.pp3779-3794

Copyright (c) 2025 Ade Iriani Sapitri, Siti Nurmaini, Muhammad Naufal Rachmatullah, Annisa Darmawahyuni, Firdaus Firdaus, Anggun Islami, Bambang Tutuko, Akhiar Wista Arum

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by theĀ Institute of Advanced Engineering and Science (IAES).