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ADASEML: Hospitalization Period Prediction of COVID-19 Patients Using ADASYN and Stacking Based Ensemble Learning Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Pixel Attention Based Deep Neural Network for Chest CT Image Super Resolution

Authors : P. Rajeshwari, K. Shyamala

Published in: Advanced Network Technologies and Intelligent Computing

Publisher: Springer Nature Switzerland

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Abstract

The High-Resolution chest CT scan images help to diagnose lung related diseases accurately. In general, the more advanced hardware used in CT Scan machines, the more high resolution images will be generated. But it is a costlier approach. This limitation can be overcome with the post processing of the images generated from the CT machine. Even when the image is upscaled, the quality of the image should be retained. So, the process of reconstructing the High-Resolution images from the Low-Resolution images is known as Image Super-Resolution. The recent advancements in hardware and Super Resolution deep neural networks enabled reconstructing High-Resolution images in an efficient way. The objective quality metric Peak-Signal-to-Noise-Ratio evaluates the performance of a SR deep model. In this paper, proposed a pixel attention based deep neural network, MediSR for chest CT scan medical image Super-Resolution. The model is trained with two chest CT datasets and the experimental results showed an improvement of 1.78% and 18.23% for the 2\(\times \) and 4\(\times \) scale factors over the existing literature.

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Literature
1.
Irani, et al.: Improving resolution by image registration. CVGIP: Graph. Model. Image Process. 53(5), 231–239 (1991)
2.
3.
Bakr, et al.: A radiogenomic dataset of non-small cell lung cancer. Scientific Data 5(1), (1–9) (2018)
4.
Zhao, et al.: COVID-CT-dataset: A CT scan dataset about COVID-19 (2020)
5.
Wang, Z., et al.: Image quality assessment: From error visibility to structural similarity. TIP (2004)
6.
7.
Lukin, et al.: Image interpolation by super-resolution. Procd of GraphiCon, vol. 2006, No. Citeseer (2006)
8.
Dai, S., et al.: SoftCuts: A soft edge smoothness prior for color image super-resolution. IEEE Trans. IP 18(5), 969–981 (2009)MathSciNetMATH
9.
Sun, J., et al.: Image super-resolution using gradient profile prior. In: IEEE Conference on CVPR, pp. 1–8 (2008)
10.
Wei, W., et al.: Single-image super-resolution based on Markov random field and contourlet transform. JEI 20(2), 023005 (2011)
11.
Hong, et al.: Super-resolution through neighbor embedding. In: Proceedings of IEEE Computer Society Conference on CVPR, p. I (2004)
12.
Yang, J., et al.: Image super-resolution via sparse representation. IEEE Trans. IP 19(11), 2861–2873 (2010)MathSciNetMATH
13.
Schulter, S., et al.: Fast and accurate image upscaling with super-resolution forests. IEEE Conference on CVPR, pp. 3791–3799 (2015)
14.
Dong, C., et al.: Image Super-Resolution Using Deep Convolutional Networks, CVPR, NE. ACM (2016)
15.
16.
Shi, et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on CVPR (2016)
17.
Kim, J., et al.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on CVPR, pp. 1646–1654 (2016)
18.
Lim, B., et al.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on CVPR (2017)
19.
Balduzzi, et al.: The shattered gradients problem: If resnets are the answer, then what is the question. In: International Conference on ML, PMLR (2017)
20.
Ledig, et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on CVPR (2017)
21.
Liu, et al.: An attention-based approach for single image super resolution. In: IEEE, ICPR (2018)
22.
Zhang, et al.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on CV (ECCV), pp. 286–301 (2018)
23.
Dai, et al.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE/CVF Conference on CVPR (2019)
24.
Zheng, et al.: Upsampling Attention Network for Single Image Super-resolution. VISIGRAPP (2021)
25.
Zhang, et al.: Hierarchical patch-based sparse representation-A new approach for resolution enhancement of 4D-CT lung data. IEEE Trans MI 31(11), 1993–2005 (2012)
26.
Yan, et al.: Super resolution in CT. IJIST 25(1), 92–101 (2015)
27.
Wang, T., et al.: Adaptive patch-based POCS approach for super resolution reconstruction of 4D-CT lung data. PMB 60(15), 5939 (2015)
28.
Tran, et al.: Example-based super-resolution for enhancing spatial resolution of medical images. In: International Conference of the IEEE EMBC (2016)
29.
Yu, et al.: Computed tomography super-resolution using convolutional neural networks. In: IEEE International Conference on IP (2017)
30.
Umehara, et al.: Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. JDI 31, 441–450 (2018)
31.
32.
Van der Ouderaa, et al.: Chest CT Super-Resolution and Domain-Adaptation Using Memory-Efficient 3D Reversible GANs (2019)
33.
Zhang, et al.: CT Super Resolution via Zero Shot Learning (2020)
34.
Park, et al.: Computed tomography super-resolution using deep convolutional neural network. PMB 63, 145011 (2018)
Metadata
Title
Pixel Attention Based Deep Neural Network for Chest CT Image Super Resolution
Authors
P. Rajeshwari
K. Shyamala
Copyright Year
2023
Book
Advanced Network Technologies and Intelligent Computing

Print ISBN: 978-3-031-28182-2

Electronic ISBN: 978-3-031-28183-9

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-28183-9_28

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