Hybrid Particle Swarm Optimized and Fuzzy C Means Clustering based segmentation technique for investigation of COVID-19 infected chest CT

References

• Abd Elaziz M, Ewees AA, Yousri D, Alwerfali HS, Awad QA, Lu S, Al-Qaness MA. 2020 Jul 8. An improved marine predators algorithm with fuzzy entropy for multi-level thresholding: Real world example of COVID-19 CT image segmentation. IEEE Access. 8:125306–125330. doi:10.1109/ACCESS.2020.3007928
   Google Scholar
• Adhikari SK, Sing JK, Basu DK, Nasipuri M. 2015 Sep 1. Conditional spatial Fuzzy C-means clustering algorithm for segmentation of MRI images. Appl Soft Comput. 34:758–769. doi:10.1016/j.asoc.2015.05.038
   Google Scholar
• Amyar A, Modzelewski R, Li H, Ruan S. 2020 Nov 1. Multi-Task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med. 126:104037. doi:10.1016/j.compbiomed.2020.104037
   Google Scholar
• Bansal JC, Singh PK, Pal NR, editors. 2019. Evolutionary and swarm intelligence algorithms. Berlin, Germany: Springer.
   Google Scholar
• Chakraborty S, Mali K. 2021 Aug 1. A morphology-based radiological image segmentation approach for efficient screening of COVID-19. Biomed Signal Process Control. 69:102800. doi:10.1016/j.bspc.2021.102800
   Google Scholar
• Chakraborty S, Mali K. 2021 Sep 15. SUFMACS: a machine learning-based robust image segmentation framework for covid-19 radiological image interpretation. Expert Syst Appl. 178:115069. doi:10.1016/j.eswa.2021.115069
   Google Scholar
• Chakraborty S, Mali K. 2021 Apr 1. SuFMoFPA: a superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images. Expert Syst Appl. 167:114142. doi:10.1016/j.eswa.2020.114142
   Google Scholar
• Chen X, Yao L, Zhang Y. 2020 Apr 12. Residual attention u-net for automated multi-class segmentation of covid-19 chest ct images. arXiv preprint arXiv:2004.05645.
   Google Scholar
• Chen C, Zhou K, Zha M, Qu X, Guo X, Chen H, Wang Z, Xiao R. 2021 Feb 12. An effective deep neural network for lung lesions segmentation from COVID-19 CT images. IEEE Trans Ind Inf. 17(9): 6528–6538. doi:10.1109/TII.2021.3059023.
   Google Scholar
• Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, Bleicker T, Brünink S, Schneider J, Schmidt ML, et al. 2020 Jan 23. Detection of 2019 novel coronavirus (2019-nCov) by real-time RT-PCR. Eurosurveillance. 25(3):2000045. doi10.2807/1560-7917.ES.2020.25.3.2000045
   Google Scholar
• Dhruv B, Mittal N, Modi M 2020 Jun 4. Medical image segmentation techniques and their relevance in contemporary imaging. In: 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India. IEEE; p. 1306–1310.
   Google Scholar
• Dhruv B, Mittal N, Modi M. 2021. Early and precise detection of pancreatic tumor by hybrid approach with edge detection and artificial intelligence techniques. EAI Endorsed Trans Pervasive Health Technol. 28 :e1.
   Google Scholar
• Dhruv B, Mittal N, Modi M. 2021 . Improved Particle Swarm Optimization for detection of pancreatic tumor using split and merge algorithm. Comput Methods Biomech Biomed Eng. 25:1.
   Google Scholar
• Du KL, Swamy MN. 2016. Particle swarm optimization. In: Du KL, Swamy MN. Search and optimization by metaheuristics. Cham: Birkhäuser; p. 153–173.
   Google Scholar
• Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W. 2020 Aug. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. 296(2):E115–7. doi:10.1148/radiol.2020200432
   Google Scholar
• Gao K, Su J, Jiang Z, Zeng LL, Feng Z, Shen H, Rong P, Xu X, Qin J, Yang Y, et al. 2021 Jan 1. Dual-Branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images. Med Image Anal. 67:101836. doi:10.1016/j.media.2020.101836
   Google Scholar
• Hani C, Trieu NH, Saab I, Dangeard S, Bennani S, Chassagnon G, Revel MP. 2020 May 1. COVID-19 pneumonia: a review of typical CT findings and differential diagnosis. Diagn Interv Imaging. 101(5):263–268. doi10.1016/j.diii.2020.03.014
   Google Scholar
• Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, Der Laak JA V, Van Ginneken B, Sánchez CI. 2017 Dec 1. A survey on deep learning in medical image analysis. Med Image Anal. 42:60–88. doi:10.1016/j.media.2017.07.005
   Google Scholar
• Müller D, Rey IS, Kramer F. 2020 Jun 24. Automated chest ct image segmentation of covid-19 lung infection based on 3d u-net. arXiv preprint arXiv:2007.04774.
   Google Scholar
• Müller D, Soto-Rey I, Kramer F. 2021 Jan 1. Robust chest CT image segmentation of COVID-19 lung infection based on limited data. Inf Med Unlocked. 25:100681. doi:10.1016/j.imu.2021.100681
   Google Scholar
• Nayak J, Naik B, Behera H. 2015. Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014. Comput Int Data Min. 2:133–149.
   Google Scholar
• Oulefki A, Agaian S, Trongtirakul T, Laouar AK. 2021 Jun 1. Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recognit. 114:107747. doi:10.1016/j.patcog.2020.107747
   Google Scholar
• Qiu Y, Liu Y, Li S, Xu J. 2020 Apr 21. Miniseg: an extremely minimum network for efficient covid-19 segmentation. arXiv preprint arXiv:2004.09750.
   Google Scholar
• Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D. 2020 Apr 16. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng. 14:4–15. doi:10.1109/RBME.2020.2987975
   Google Scholar
• Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N. 2021 Jun 29. Deep learning models for COVID-19 infected area segmentation in CT images. In: InThe 14th PErvasive Technologies Related to Assistive Environments Conference; p. 404–411.
   Google Scholar
• Wang B, Jin S, Yan Q, Xu H, Luo C, Wei L, Zhao W, Hou X, Ma W, Xu Z, et al. 2021 Jan 1. AI-Assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. Appl Soft Comput. 98:106897. doi:10.1016/j.asoc.2020.106897
   Google Scholar
• Wang G, Liu X, Li C, Xu Z, Ruan J, Zhu H, Meng T, Li K, Huang N, Zhang S. 2020 Jun 5. A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging. 39(8):2653–2663. doi10.1109/TMI.2020.3000314
   Google Scholar
• Wang Y, Zhang Y, Liu Y, Tian J, Zhong C, Shi Z, Zhang Y, He Z. 2021 Apr 1. Does non-COVID-19 lung lesion help? Investigating transferability in COVID-19 CT image segmentation. Comput Methods Programs Biomed. 202:106004. doi:10.1016/j.cmpb.2021.106004
   Google Scholar
• Wu YH, Gao SH, Mei J, Xu J, Fan DP, Zhang RG, Cheng MM. 2021 Feb 18. Jcs: an explainable covid-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process. 30:3113–3126. doi:10.1109/TIP.2021.3058783
   Google Scholar
• Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, Ai J, Shi Q, Zhang Y, Jin S, et al. 2021 COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations. IEEE Trans Big Data. 7(1):13–24. doi10.1109/TBDATA.2021.3056564
   Google Scholar
• Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, Shi Q, Jin S, Zhang L, You Z. 2020 Apr 23. COVID-19 chest CT image segmentation–a deep convolutional neural network solution. arXiv preprint arXiv:2004.10987.
   Google Scholar
• Zhao S, Li Z, Chen Y, Zhao W, Xie X, Liu J, Zhao D, Li Y. 2021 . SCOAT-Net: a novel network for segmenting COVID-19 lung opacification from CT images. Pattern Recognit. 10:108109. doi:10.1016/j.patcog.2021.108109
   Google Scholar
• Zhao X, Zhang P, Song F, Fan G, Sun Y, Wang Y, Tian Z, Zhang L, Zhang G. 2021 Jun 2. D2A U-Net: automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution. Comput Biol Med:104526. doi:10.1016/j.compbiomed.2021.104526
   Google Scholar
• Zhu Z, Xingming Z, Tao G, Dan T, Li J, Chen X, Li Y, Zhou Z, Zhang X, Zhou J, et al. 2021 Mar. Classification of COVID-19 by compressed chest CT image through deep learning on a large patients cohort. Interdiscip Sci. 13(1):73–82.
   Google Scholar