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

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Abstract

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

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

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.




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Acceptance to publication20-30 days

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