Acta Scientific Computer Sciences

Research Article Volume 4 Issue 6

Efficient Super-resolution For Chest X-rays

Karthik Sivarama Krishnan* and Koushik Sivarama Krishnan

Gen Nine Inc, United States

*Corresponding Author: Karthik Sivarama Krishnan, Gen Nine Inc, United States.

Received: April 30, 2022; Published: May 18, 2022


High-resolution images are really helpful in various applications like medical diagnosis and hence the need for super-resolution has also increased significantly. Increasing the image resolution on various medical images like a chest X-ray or cell images can improve the accuracy of diagnosis by revealing previously unseen details. Using Image super-resolution also reduces the number of X-ray radiations required to render ultra high-quality imaging. Hence we applied super-resolution on X-rays using fine-tuned Swift-SRGAN architecture, which significantly improved the details on the chest X-rays. This helps in rendering super-resolution images from low-resolution images with less computational requirements. The proposed approach achieves a Structural Similarity Index Measure(SSIM) of 0.893 and a Peak Signal-to-Noise Ratio (PSNR) of 32.10.

Keywords: X-rays; Super-resolution; Swift-SRGAN; Generative Adversarial Network (GAN); Chest X-ray; Medical Imaging; Mobile Computing; Peak Signal-to-Noise Ratio (PSNR); Structural Similarity Index Measure (SSIM)


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Citation: Karthik Sivarama Krishnan and Koushik Sivarama Krishnan. “Efficient Super-resolution For Chest X-rays". Acta Scientific Computer Sciences 4.6 (2022): 46-50 .


Copyright: © 2022 Karthik Sivarama Krishnan and Koushik Sivarama Krishnan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
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