DeSa COVID-19: Deep Salient COVID-19 Image-based Quality Assessment

Overview

Journal J King Saud Univ Comput Inf Sci

Date 2024 Apr 15

PMID 38620925

Authors

Risnandar

Affiliations

Soon will be listed here.

Abstract

This study offers an advanced method to evaluate the coronavirus disease 2019 (COVID-19) image quality. The salient COVID-19 image map is incorporated with the deep convolutional neural network (DCNN), namely DeSa COVID-19, which exerts the n-convex method for the full-reference image quality assessment (FR-IQA). The glaring outcomes substantiate that DeSa COVID-19 and the recommended DCNN architecture can convey a remarkable accomplishment on the COVID-chestxray and the COVID-CT datasets, respectively. The salient COVID-19 image map is also gauged in the minuscule COVID-19 image patches. The exploratory results attest that DeSa COVID-19 and the recommended DCNN methods are very good accomplishment compared with other advanced methods on COVID-chestxray and COVID-CT datasets, respectively. The recommended DCNN also acquires the enhanced outgrowths faced with several advanced full-reference-medical-image-quality-assessment (FR-MIQA) techniques in the fast fading (FF), blocking artifact (BA), white noise Gaussian (WG), JPEG, and JPEG2000 (JP2K) in the distorted and undistorted COVID-19 images. The Spearman's rank order correlation coefficient (SROCC) and the linear correlation coefficient (LCC) appraise the recommended DCNN and DeSa COVID-19 fulfillment which are compared the recent FR-MIQA methods. The DeSa COVID-19 evaluation outshines and higher compared the recommended DCNN, and and esteem all of advanced FR-MIQAs methods on SROCC and LCC measures, respectively. The shift add operations of trigonometric, logarithmic, and exponential functions are mowed down in the computational complexity of the DeSa COVID-19 and the recommended DCNN. The DeSa COVID-19 more superior the recommended DCNN and also the other recent full-reference medical image quality assessment methods.

References

Wang S, Govindaraj V, Gorriz J, Zhang X, Zhang Y . Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf Fusion. 2020; 67:208-229. PMC: 7544601. DOI: 10.1016/j.inffus.2020.10.004. View

Shen J, Peng J, Dong X, Shao L, Porikli F . Higher Order Energies for Image Segmentation. IEEE Trans Image Process. 2017; 26(10):4911-4922. DOI: 10.1109/TIP.2017.2722691. View

Zhang Y, Satapathy S, Zhu L, Gorriz J, Wang S . A Seven-Layer Convolutional Neural Network for Chest CT-Based COVID-19 Diagnosis Using Stochastic Pooling. IEEE Sens J. 2022; 22(18):17573-17582. PMC: 9564037. DOI: 10.1109/JSEN.2020.3025855. View

Shiao Y, Chen T, Chuang K, Lin C, Chuang C . Quality of compressed medical images. J Digit Imaging. 2007; 20(2):149-59. PMC: 3043905. DOI: 10.1007/s10278-007-9013-z. View

Lang C, Feng J, Feng S, Wang J, Yan S . Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection. IEEE Trans Neural Netw Learn Syst. 2016; 27(6):1190-200. DOI: 10.1109/TNNLS.2015.2513393. View

Huo D, Xu D, Liang Z, Wilson D . Application of perceptual difference model on regularization techniques of parallel MR imaging. Magn Reson Imaging. 2006; 24(2):123-32. DOI: 10.1016/j.mri.2005.10.018. View

Chen T, Lin L, Liu L, Luo X, Li X . DISC: Deep Image Saliency Computing via Progressive Representation Learning. IEEE Trans Neural Netw Learn Syst. 2016; 27(6):1135-49. DOI: 10.1109/TNNLS.2015.2506664. View

Liu N, Han J . A Deep Spatial Contextual Long-Term Recurrent Convolutional Network for Saliency Detection. IEEE Trans Image Process. 2018; 27(7):3264-3274. DOI: 10.1109/TIP.2018.2817047. View

Wang W, Shen J . Deep Visual Attention Prediction. IEEE Trans Image Process. 2018; 27(5):2368-2378. DOI: 10.1109/TIP.2017.2787612. View

10.

Shen J, Peng J, Shao L . Submodular Trajectories for Better Motion Segmentation in Videos. IEEE Trans Image Process. 2018; . DOI: 10.1109/TIP.2018.2795740. View

11.

. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol. 2020; 5(4):536-544. PMC: 7095448. DOI: 10.1038/s41564-020-0695-z. View

12.

Wang Z, Bovik A, Sheikh H, Simoncelli E . Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004; 13(4):600-12. DOI: 10.1109/tip.2003.819861. View

13.

Wang S, Nayak D, Guttery D, Zhang X, Zhang Y . COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Inf Fusion. 2021; 68:131-148. PMC: 7837204. DOI: 10.1016/j.inffus.2020.11.005. View

14.

Hou Q, Cheng M, Hu X, Borji A, Tu Z, Torr P . Deeply Supervised Salient Object Detection with Short Connections. IEEE Trans Pattern Anal Mach Intell. 2018; 41(4):815-828. DOI: 10.1109/TPAMI.2018.2815688. View

15.

Xue W, Zhang L, Mou X, Bovik A . Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index. IEEE Trans Image Process. 2015; 23(2):684-95. DOI: 10.1109/TIP.2013.2293423. View

16.

Wang Z, Li Q . Information content weighting for perceptual image quality assessment. IEEE Trans Image Process. 2010; 20(5):1185-98. DOI: 10.1109/TIP.2010.2092435. View

17.

Moorthy A, Bovik A . Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans Image Process. 2011; 20(12):3350-64. DOI: 10.1109/TIP.2011.2147325. View

18.

Sheikh H, Bovik A, De Veciana G . An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans Image Process. 2005; 14(12):2117-28. DOI: 10.1109/tip.2005.859389. View

19.

Wang W, Shen J, Ling H . A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping. IEEE Trans Pattern Anal Mach Intell. 2018; 41(7):1531-1544. DOI: 10.1109/TPAMI.2018.2840724. View

20.

Gao Y, Wang M, Zha Z, Shen J, Li X, Wu X . Visual-textual joint relevance learning for tag-based social image search. IEEE Trans Image Process. 2012; 22(1):363-76. DOI: 10.1109/TIP.2012.2202676. View