[1] M. Abdulghafour, Image segmentation using Fuzzy logic and genetic algorithms, J. WSCG 11 (2003), no. 1.
[2] S. Akram and Q.U. Ann, Newton raphson method, Int. J. Sci. Eng. Res. 6 (2015), no. 7, 1748–1752.
[3] L.J. Belaid and W. Mourou, Image segmentation: a watershed transformation algorithm, Image Anal. Stereol. 28 (2009), 93–103.
[4] S. Biswas, D. Ghoshal and R. Hazra, A new algorithm of image segmentation using curve fitting based higher order polynomial smoothing, Optik 127 (2016), no. 20, 8916–8925.
[5] A.C. Brooks, X. Zhao and T.N. Pappas, Structural similarity quality metrics in a coding context: exploring the space of realistic distortions, IEEE Trans. Image Process. 17 (2008).
[6] Y. Cao, Z. Xu, J. Feng, C. Jin, X. Han, H. Wu and H. Shi, Longitudinal assessment of COVID-19 using a deep learning–based quantitative CT pipeline: illustration of two cases, Radiology: Cardioth. Imag. 2 (2020), e200082.
[7] J.P. Cohen, P. Morrison and L. Dao, COVID-19 image data collection, arXiv 2003.11597, (2020).
[8] D. Datta, S. Mishra and S.S. Rajest, Quantification of tolerance limits of engineering system using uncertainty modeling for sustainable energy, Int. J. Intell. Networks 1 (2020), 1–8.
[9] B. Ghoshal and A. Tucker, Estimating uncertainty and interpretability in deep learning for coronavirus (COVID19) detection, arXiv:2003.10769, (2020).
[10] R.C. Gonzalez and P. Wintz, Digital image processing, 2nd ed. Addison-Wesley, Reading, MA, 1987.
[11] D.L.L. Gwet, M. Otesteanu, I.O. Libouga, L. Bitjoka and G.D. Popa, A review on image segmentation techniques and performance measures, Int. J. Comput. Inf. Eng. 12 (2018), no. 12, 1107–1117.
[12] A. Haldorai, A. Ramu and S. Murugan, Social aware cognitive radio networks: effectiveness of social networks as a strategic tool for organizational business management, Social network analytics for contemporary business organizations, IGI Global, 2018, pp. 18–202.
[13] V. Hariraj, W. Khairunizam, V. Vikneswaran, Z. Ibrahim, A.B. Shahriman, M.R. Zuradzman, T. Rajendran and R. Sathiyasheelan, Fuzzy multi-layer SVM classification of breast cancer mammogram images, Int. J. Mechanic. Eng. Technol. 9 (2018), no. 8, 1281–1299.
[14] A.T. Hashim and D.A. Noori, An approach of noisy color iris segmentation based on hybrid image processing techniques, Int. Conf. Cyberworlds (CW), IEEE, 2016, pp. 183–188.
[15] L. Huang, R. Han, T. Ai, P. Yu, H. Kang, Q. Tao and L. Xia, Serial quantitative chest CT assessment of COVID-19: deep-learning approach, Radiology: Cardioth. Imag. 2 (2020), e200075.
[16] M.S. Khadem, MRI brain image segmentation using graph cuts, Master’s thesis, Chalmers University of Technology, Goteborg, Sweden, 2010.
[17] I.G. Khanykov, I.M. Tolstoj and D.K. Levonevskiy, The classification of the image segmentation algorithms, Int. J. Intell. Unmanned Syst. 8 (2020), no. 2, 115–127.
[18] E.L. Lidiya and S. Kannan, S.S. Rajest and S. Satyanarayana, Correlative study and analysis for hidden patterns in text analytics unstructured data using supervised and unsupervised learning techniques, Int. J. Cloud Comput. 9 (2020), no. 2/3.
[19] A. Liu, W. Lin and M. Narwaria, Image quality assessment based on gradient similarity, IEEE Trans. Image
Process. 21 (2012), no. 4, 1500–1512.
[20] D.R. Martin, C.C. Fowlkes and J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Trans. Pattern Anal. Machine Intel. 26 (2004), no. 5, 530–549.
[21] P.R. Misra and T. Si, Image segmentation using clustering with fireworks algorithm, 11th Int. Conf. Intel. Syst. Control (ISCO), IEEE, 2017, pp. 97–102.
[22] H.M. Moftah, A. TaherAzar, E.T. Al-Shammari, N.I. Ghali, A.E. Hassanien and M. Shoman, Adaptive k-means clustering algorithm for MR breast image segmentation, Neural Comput. Applic. 24 (2014), 1917–1928.
[23] M. Nachtegael, D. Van der Weken, D. Van De Ville and E.E. Kerre, Fuzzy filters for image processing, SpringerVerlag Berlin Heidelberg, 2003.
[24] A.J. Obaid, Critical research on the novel progressive, JOKER an opportunistic routing protocol technology for enhancing the network performance for multimedia communications, Res. Intell. Comput. Eng. Adv. Intell. Syst. Comput. 1254 (2021), 369–378.
[25] O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y. Hammerla, B. Kainz and B. Glocker, Attention U-Net: Learning where to look for the pancreas, arXiv:1804.03999, (2018).
[26] X. Qi, Z. Jiang, Q. Yu, C. Shao, H. Zhang, H. Yue, B. Ma, Y. Wang, C. Liu, X. Meng and S. Huang, Nachine learning-based CT radionics model for predicting hospital stay in patients with pneumonia associated with SARSCoV-2 infection: A multicenter study, MedRxiv, 2020.
[27] K.K.D. Ramesh, G.K. Kumar, K. Swapna, D. Datta and S.S. Rajest, A review of medical image segmentation algorithms, EAI Endorsed Trans. Pervasive Health Technol. 7 (2021), no. 27, e6.
[28] O. Ronneberger, P. Fischer and T. Brox, U-Net: Convolutional networks for biomedical image segmentation, Int. Conf. Medical Image Comput. Comput.-Assist. Intervent., 2015, pp. 234–241.
[29] L. Santoso, B. Singh, S.S. Rajest, R. Regin, K. Kadhim, A genetic programming approach to binary classification problem, EAI Endorsed Trans. Energy 8 (2021), no. 31, 1–8.
[30] P.D. Sathya and R. Kayalvizhi, Optimal multilevel thresholding using bacterial foraging algorithm, Expert. Syst. Appl. 38 (2011), no. 12, 15549–15564.
[31] N. Senthilkumaran and R. Rajesh, Edge detection techniques for image segmentation—a survey, Proc. Int. Conf. Manag. Next Gener. Software Appl. (MNGSA-08), 2008, pp. 749–760.
[32] S. Sharma and A.J. Obaid, Mathematical modelling, analysis and design of fuzzy logic controller for the control of ventilation systems using MATLAB fuzzy logic toolbox, J. Interdiscip. Math. 23 (2020), no. 4, 843–849.
[33] P. Sharma and J. Suji, A review on image segmentation with its clustering techniques, Int. J. Signal Process. Image Process. Pattern Recogn. 9 (2016), no. 5, 209–218.
[34] F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, K. He, Y. Shi and D. Shen, Review of artificial intelligence techniques in imaginf data acquisition, segmentation, and diagnosis for COVID-19, IEEE Rev. Biomed. Eng. 14 (2020), 4–15.
[35] B. Singh, P. Kavitha, R. Regin, K. Praghash, S. Sujatha and S.S. Rajest, Optimized node clustering based on received signal strength with particle ordered-filter routing used in VANET, Webology 17 (2020), no. 2, 262–277.
[36] M. Suganya and H. Anandakumar, Handover based spectrum allocation in cognitive radio networks, Int. Conf. Green Comput. Commun. Conserv. Energy (ICGCE), 2013.
[37] O.J. Tobias and R. Seara, Image segmentation by histogram thresholding using fuzzy sets, IEEE Trans. Image Process. 11 (2002), no. 12, 1457–1465.
[38] M. Xess and S.A. Agnes, Survey on clustering based color image segmentation and novel approaches to FCM algorithm, Int. J. Res. Eng. Technol. 2013 (2013), 346–349.
[39] J. Zhang and J. Hu, Image segmentation based on 2D Otsu method with histogram analysis, IEEE, Int. Conf. Comput. Sci. Software Eng. 6 (2008).
[40] C. Zheng, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, W. Liu and X. Wang, Deep learning-based detection for COVID-19 from chest CT using weak label, MedRxiv, 2020.
[41] H. Zhu, F. Meng, J. Cai and S. Lu, Beyond pixels: a comprehensive survey from bottom-up to semantic image segmentation and cosegmentation, J. Visual Commun. Image Represent. 34 (2016), 12–27.