{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:00:59Z","timestamp":1778662859614,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["JB210402"],"award-info":[{"award-number":["JB210402"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["XJS210406"],"award-info":[{"award-number":["XJS210406"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2021JQ199"],"award-info":[{"award-number":["2021JQ199"]}]},{"name":"Shaanxi Provincial Natural Science Foundation","award":["JB210402"],"award-info":[{"award-number":["JB210402"]}]},{"name":"Shaanxi Provincial Natural Science Foundation","award":["XJS210406"],"award-info":[{"award-number":["XJS210406"]}]},{"name":"Shaanxi Provincial Natural Science Foundation","award":["2021JQ199"],"award-info":[{"award-number":["2021JQ199"]}]},{"name":"Intelligent Robot Laboratory of Hangzhou Institute of Technology (HIT) of Xidian University","award":["JB210402"],"award-info":[{"award-number":["JB210402"]}]},{"name":"Intelligent Robot Laboratory of Hangzhou Institute of Technology (HIT) of Xidian University","award":["XJS210406"],"award-info":[{"award-number":["XJS210406"]}]},{"name":"Intelligent Robot Laboratory of Hangzhou Institute of Technology (HIT) of Xidian University","award":["2021JQ199"],"award-info":[{"award-number":["2021JQ199"]}]},{"name":"Xi\u2019an Theory and Application of Discrete Event Dynamic Systems International Science and Technology Cooperation Center","award":["JB210402"],"award-info":[{"award-number":["JB210402"]}]},{"name":"Xi\u2019an Theory and Application of Discrete Event Dynamic Systems International Science and Technology Cooperation Center","award":["XJS210406"],"award-info":[{"award-number":["XJS210406"]}]},{"name":"Xi\u2019an Theory and Application of Discrete Event Dynamic Systems International Science and Technology Cooperation Center","award":["2021JQ199"],"award-info":[{"award-number":["2021JQ199"]}]},{"name":"Complex Systems International Joint Research Center of Shaanxi Province","award":["JB210402"],"award-info":[{"award-number":["JB210402"]}]},{"name":"Complex Systems International Joint Research Center of Shaanxi Province","award":["XJS210406"],"award-info":[{"award-number":["XJS210406"]}]},{"name":"Complex Systems International Joint Research Center of Shaanxi Province","award":["2021JQ199"],"award-info":[{"award-number":["2021JQ199"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Medical optical imaging, with the aid of the \u201cterahertz tomography\u201d, is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis for better verification and validation. Two important aspects, the analytic approach for the understanding of the Schrodinger transforms and machine learning approaches for the understanding of the medical images segmentation, are presented in this manuscript. While developing an AI algorithm for complex datasets, the computational speed and accuracy cannot be overlooked. With the passage of time, machine learning approaches have been further modified using the Bayesian, genetic and quantum approaches. These strategies have boosted the efficiency of the machine learning, and specifically the deep learning tools, by taking into account the probabilistic, evolutionary and quantum qubits hypothesis and operations, respectively. The current research encompasses the detailed analysis of image segmentation algorithms based on the evolutionary approach. The image segmentation algorithm that converts the color model from RGB to HSI and the image segmentation algorithm that uses the clustering technique are discussed in detail, and further extensions of these genetic algorithms to quantum algorithms are proposed. Based on the genetic algorithm, the optimal selection of parameters is realized so as to achieve a better segmentation effect.<\/jats:p>","DOI":"10.3390\/sym14101977","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T04:07:07Z","timestamp":1663906027000},"page":"1977","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Comparative Study of the Genetic Deep Learning Image Segmentation Algorithms"],"prefix":"10.3390","volume":"14","author":[{"given":"Wenbo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Yousaf","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Comsats University Islamabad, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0159-8545","authenticated-orcid":false,"given":"Ding","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Intelligent Robot Laboratory, Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6835-6212","authenticated-orcid":false,"given":"Ayesha","family":"Sohail","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Comsats University Islamabad, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1088\/0034-4885\/61\/2\/002","article-title":"Quantum computing","volume":"61","author":"Steane","year":"1998","journal-title":"Rep. Prog. Phys."},{"key":"ref_2","first-page":"19","article-title":"A study of complex deep learning networks on high-performance, neuromorphic, and quantum computers","volume":"14","author":"Potok","year":"2018","journal-title":"ACM J. Emerg. Technol. Comput. Syst. (JETC)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s10614-021-10110-z","article-title":"Quantum computing and deep learning methods for GDP growth forecasting","volume":"59","author":"Alaminos","year":"2022","journal-title":"Comput. Econ."},{"key":"ref_4","first-page":"1590","article-title":"Medical Image Segmentation","volume":"760\u2013762","author":"Prasantha","year":"2010","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.media.2009.05.004","article-title":"Statistical shape models for 3D medical image segmentation: A review","volume":"13","author":"Heimann","year":"2009","journal-title":"Med. Image Anal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sohail, A., Fahmy, M.A., and Khan, U.A. (2022). XAI hybrid multi-staged algorithm for routine & quantum boosted oncological medical imaging. Comput. Part. Mech., 1\u201311.","DOI":"10.1007\/s40571-022-00490-w"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1950030","DOI":"10.4015\/S1016237219500303","article-title":"Inference of biomedical data sets using Bayesian machine learning","volume":"31","author":"Sohail","year":"2019","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.pbiomolbio.2019.11.012","article-title":"Supervised and unsupervised algorithms for bioinformatics and data science","volume":"151","author":"Sohail","year":"2020","journal-title":"Prog. Biophys. Mol. Biol."},{"key":"ref_9","unstructured":"Sohail, A. (2021). Genetic algorithms in the fields of artificial intelligence and data sciences. Ann. Data Sci., 1\u201312."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127207","DOI":"10.1016\/j.physa.2022.127207","article-title":"SEI2RS malware propagation model considering two infection rates in cyber\u2013physical systems","volume":"597","author":"Yu","year":"2022","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2150033","DOI":"10.4015\/S1016237221500332","article-title":"AI Optimization of the Exothermic Reaction of Ethylene Oxide with Water","volume":"33","author":"Sohail","year":"2021","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_12","first-page":"2250019","article-title":"Artificial intelligence to link environmental endocrine disruptors (EEDs) with bone diseases","volume":"13","author":"Idrees","year":"2021","journal-title":"Int. J. Model. Simulation, Sci. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Al-Utaibi, K.A., Sohail, A., Arif, F., Celik, S., Sait, S.M., and Keskin, D.B. (2022). Neural networks to understand the physics of oncological medical imaging. Biomed. Eng. Appl. Basis Commun., 2250036.","DOI":"10.4015\/S1016237222500363"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.clinbiomech.2019.12.015","article-title":"A computational framework and sensitivity analysis for the hormonal treatment of bone","volume":"73","author":"Idrees","year":"2020","journal-title":"Clin. Biomech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"012082","DOI":"10.1088\/1742-6596\/1955\/1\/012082","article-title":"Image Segmentation of Pitaya Disease Based on Genetic Algorithm and Otsu Algorithm","volume":"1955","author":"Yan","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_16","unstructured":"Lakshmi, V.K., Feroz, C.A., and Merlin, J. (2018, January 13\u201314). Automated Detection and Segmentation of Brain Tumor Using Genetic Algorithm. Proceedings of the 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.media.2016.06.023","article-title":"Four Challenges in Medical Image Analysis from an Industrial Perspective","volume":"33","author":"Weese","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110222","DOI":"10.1016\/j.chaos.2020.110222","article-title":"Stabilization of single-and multi-peak solitons in the fractional nonlinear Schr\u00f6dinger equation with a trapping potential","volume":"140","author":"Qiu","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1007\/s11128-015-1072-3","article-title":"A quantum mechanics-based framework for image processing and its application to image segmentation","volume":"14","author":"Youssry","year":"2015","journal-title":"Quantum Inf. Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Aytekin, \u00c7., Kiranyaz, S., and Gabbouj, M. (2013, January 15\u201318). Quantum mechanics in computer vision: Automatic object extraction. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738513"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.colsurfa.2011.07.009","article-title":"Analysis of capillary-gravity waves using the discrete periodic inverse scattering transform","volume":"391","author":"Sohail","year":"2011","journal-title":"Colloids Surf. A Physicochem. Eng. Asp."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105046","DOI":"10.1016\/j.rinp.2021.105046","article-title":"Piecewise differentiation of the fractional order CAR-T cells-SARS-2 virus model","volume":"33","author":"Sohail","year":"2021","journal-title":"Results Phys."},{"key":"ref_23","first-page":"S0218348X22401223","article-title":"Explainability of neural network clustering in interpreting the COVID-19 emergency data","volume":"10","author":"Yu","year":"2021","journal-title":"Fractals"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1007\/s11071-021-06777-6","article-title":"Forecasting the impact of environmental stresses on the frequent waves of COVID19","volume":"106","author":"Yu","year":"2021","journal-title":"Nonlinear Dyn."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105892","DOI":"10.1016\/j.rinp.2022.105892","article-title":"Crossover behaviour of the Zika virus infection and the delayed immune response","volume":"41","author":"Arif","year":"2022","journal-title":"Results Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104282","DOI":"10.1016\/j.rinp.2021.104282","article-title":"Dynamical analysis of the delayed immune response to cancer","volume":"26","author":"Sohail","year":"2021","journal-title":"Results Phys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"585245","DOI":"10.3389\/fmolb.2020.585245","article-title":"Delayed Modeling Approach to Forecast the Periodic Behavior of SARS-2","volume":"7","author":"Yu","year":"2021","journal-title":"Front. Mol. Biosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"114863","DOI":"10.1016\/j.molliq.2020.114863","article-title":"Modeling and simulations of CoViD-19 molecular mechanism induced by cytokines storm during SARS-CoV2 infection","volume":"327","author":"Yu","year":"2021","journal-title":"J. Mol. Liq."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111202","DOI":"10.1016\/j.chaos.2021.111202","article-title":"Self organizing maps for the parametric analysis of COVID-19 SEIRS delayed model","volume":"150","author":"Yu","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1515\/bams-2020-0054","article-title":"Bio-algorithms for the modeling and simulation of cancer cells and the immune response","volume":"17","author":"Idrees","year":"2021","journal-title":"Bio-Algorithms Med-Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lou, L., Zeng, H., Xiong, J., Li, L., and Gao, W. (2012). Schr\u00f6dinger transform of image: A new tool for image analysis. Measurements in Quantum Mechanics, Books on Demand.","DOI":"10.5772\/34507"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chahid, A., Serrai, H., Achten, E., and Laleg-Kirati, T.M. (2017, January 19\u201321). Adaptive method for MRI enhancement using squared eigenfunctions of the Schr\u00f6dinger operator. Proceedings of the 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), Torino, Italy.","DOI":"10.1109\/BIOCAS.2017.8325107"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117525","DOI":"10.1016\/j.neuroimage.2020.117525","article-title":"Schr\u00f6dinger filtering: A precise EEG despiking technique for EEG-fMRI gradient artifact","volume":"226","author":"Benigno","year":"2021","journal-title":"NeuroImage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105774","DOI":"10.1016\/j.rinp.2022.105774","article-title":"Modeling the crossover behavior of the bacterial infection with the COVID-19 epidemics","volume":"39","author":"Yu","year":"2022","journal-title":"Results Phys."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.13031\/2013.2970","article-title":"Color image segmentation with genetic algorithm for in-field weed sensing","volume":"43","author":"Tang","year":"2000","journal-title":"Trans. ASAE"},{"key":"ref_36","unstructured":"Oliveira, P., and Yamanaka, K. (2018, January 21\u201323). Image Segmentation Using Multilevel Thresholding and Genetic Algorithm: An Approach. Proceedings of the 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), Changsha, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yoshinari, K., Hoshi, Y., and Taguchi, A. (2014, January 21\u201323). Color image enhancement in HSI color space without gamut problem. Proceedings of the 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), Athens, Greece.","DOI":"10.1109\/ISCCSP.2014.6877941"},{"key":"ref_38","unstructured":"(2022, August 01). Selva. Color Image Segmentation Using Genetic Algorithm(Clustering). MATLAB Central File Exchange. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/64223-color-image-segmentation-using-genetic-algorithm-clustering."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.procs.2015.06.090","article-title":"Image Segmentation Using K-means Clustering Algorithm and Subtractive Clustering Algorithm","volume":"54","author":"Dhanachandra","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.procs.2018.05.006","article-title":"An Improved Method for Image Segmentation Using K-Means Clustering with Neutrosophic Logic","volume":"132","author":"Qureshi","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., de Lange, T., Johansen, D., and Johansen, H.D. (2020, January 5\u20138). Kvasir-seg: A segmented polyp dataset. Proceedings of the International Conference on Multimedia Modeling, Daejeon, Korea.","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"ref_42","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., and Marchetti, M. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"180160","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (2018, January 4\u20137). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, K., Fathan, M.I., Patel, K., Zhang, T., Zhong, C., Bansal, A., Rastogi, A., Wang, J.S., and Wang, G. (2021). Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations. 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