{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T20:03:21Z","timestamp":1770062601689,"version":"3.49.0"},"reference-count":36,"publisher":"American Institute of Mathematical Sciences (AIMS)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MBE"],"published-print":{"date-parts":[[2021]]},"DOI":"10.3934\/mbe.2021208","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T06:32:09Z","timestamp":1623047529000},"page":"4146-4161","source":"Crossref","is-referenced-by-count":17,"title":["Intelligent immune clonal optimization algorithm for pulmonary nodule classification"],"prefix":"10.3934","volume":"18","author":[{"given":"Qi","family":"Mao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuguang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijia","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongbing","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"ABB Electrical Machines Ltd","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2021208-1","doi-asserted-by":"crossref","unstructured":"W. L. Bi, A. Hosny, M. B. Schabath, M. L. Giger, N. J. Birkbak, A. Mehrtash, Artificial intelligence in cancer imaging: clinical challenges and applications, <i>CA: Cancer J. Clin.<\/i>, <b>69<\/b> (2019), 127-150.","DOI":"10.3322\/caac.21552"},{"key":"key-10.3934\/mbe.2021208-2","doi-asserted-by":"publisher","unstructured":"Q. Mao, S. Zhao, D. Tong, S. Su, Z. Li, X. Cheng, Hessian-MRLoG: Hessian information and multi-scale reverse LoG filter for pulmonary nodule detection, <i>Comput. Biol. Med.<\/i>, <b>131<\/b> (2021), 104272.","DOI":"10.1016\/j.compbiomed.2021.104272"},{"key":"key-10.3934\/mbe.2021208-3","doi-asserted-by":"publisher","unstructured":"X. Q. L\u00fc, L. Wu, Y. Gu, W. L. Zhang, J. Li, Detection of low dose CT pulmonary nodules based on 3D convolution neural network, <i>Opt. Precis. Eng.<\/i>, <b>26<\/b> (2018), 1211-1218.","DOI":"10.3788\/OPE.20182605.1211"},{"key":"key-10.3934\/mbe.2021208-4","doi-asserted-by":"crossref","unstructured":"B. R. Froz, A. O. de C. Filho, A.C. Silva, A. C. de Paiva, R. A. Nunes, M. Gattass, Lung nodule classification using artificial crawlers, directional texture and support vector machine, <i>Expert Syst. Appl.<\/i>, <b>69<\/b> (2017), 176-188.","DOI":"10.1016\/j.eswa.2016.10.039"},{"key":"key-10.3934\/mbe.2021208-5","doi-asserted-by":"publisher","unstructured":"C. Lu, Z. Zhu, X. Gu, An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method, <i>J. Med. Syst.<\/i>, <b>38<\/b> (2014), 97.","DOI":"10.1007\/s10916-014-0097-y"},{"key":"key-10.3934\/mbe.2021208-6","doi-asserted-by":"publisher","unstructured":"A. Abid, M. T. Khan, M. S. Khan, Multidomain features-based GA optimized artificial immune system for bearing fault detection, <i>IEEE Trans. Syst., Man Cybern. Syst.<\/i>, <b>50<\/b> (2020), 348-359.","DOI":"10.1109\/TSMC.2017.2746762"},{"key":"key-10.3934\/mbe.2021208-7","doi-asserted-by":"publisher","unstructured":"J. Gao, Q. Jiang, B. Zhou, D. Chen, Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview, <i>Math. Biosci. Eng.<\/i>, <b>16<\/b> (2019): 6536-6561.","DOI":"10.3934\/mbe.2019326"},{"key":"key-10.3934\/mbe.2021208-8","doi-asserted-by":"crossref","unstructured":"Z. Wang, X. Tang, H. Liu, L. Peng, Artificial immune intelligence-inspired dynamic real-time computer forensics model, <i>Math. Biosci. Eng.<\/i>, <b>17<\/b> (2020), 7221-7233.","DOI":"10.3934\/mbe.2020370"},{"key":"key-10.3934\/mbe.2021208-9","doi-asserted-by":"publisher","unstructured":"T. Gong, T. Fan, L. Pei, Z. Cai, Magnetic resonance imaging-clonal selection algorithm: An intelligent adaptive enhancement of brain image with an improved immune algorithm, <i>Eng. Appl. Artif. Intell.<\/i>, <b>62<\/b> (2017), 405-411.","DOI":"10.1016\/j.engappai.2016.10.004"},{"key":"key-10.3934\/mbe.2021208-10","doi-asserted-by":"crossref","unstructured":"D. Gonz\u00e1lez-Pati\u00f1 o, Y. Villuendas-Rey, A. J. Arg\u00fcelles-Cruz, O. Camacho-Nieto, C. Y\u00e1\u00f1 ez-M\u00e1rquez, AISAC: an artificial immune system for associative classification applied to breast cancer detection, <i>Appl. Sci.<\/i>, <b>10<\/b> (2020), 1-22.","DOI":"10.3390\/app10020515"},{"key":"key-10.3934\/mbe.2021208-11","doi-asserted-by":"publisher","unstructured":"L. Liu, Q. Dou, H. Chen, J. Qin, P. A. Heng, Multi-task deep model with margin ranking loss for lung nodule analysis, <i>IEEE Trans. Med. Imaging<\/i>, <b>39<\/b> (2020), 718-728.","DOI":"10.1109\/TMI.2019.2934577"},{"key":"key-10.3934\/mbe.2021208-12","doi-asserted-by":"publisher","unstructured":"Z. Zhou, S. Li, G. Qin, M. Folkert, S. Jiang, J. Wang, Multi-objective-based radiomic feature selection for lesion malignancy classification, <i>IEEE J. Biomed. Health Inf.<\/i>, <b>24<\/b> (2020), 194-204.","DOI":"10.1109\/JBHI.2019.2902298"},{"key":"key-10.3934\/mbe.2021208-13","doi-asserted-by":"publisher","unstructured":"J. Z. Farkas, G. T. Smith, G. F. Webb, A dynamic model of CT scans for quantifying doubling time of ground glass opacities using histogram analysis, <i>Math. Biosci. Eng.<\/i>, <b>15<\/b> (2018), 1203-1224.","DOI":"10.3934\/mbe.2018055"},{"key":"key-10.3934\/mbe.2021208-14","doi-asserted-by":"crossref","unstructured":"A. M. Santos, A. Filho, A. C. D. Paiva, R. A. Nunesb, M. Gattassc, Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, tsallis entropy and SVM, <i>Eng. Appl. Artif. Intell.<\/i>, <b>36<\/b> (2014), 27-39.","DOI":"10.1016\/j.engappai.2014.07.007"},{"key":"key-10.3934\/mbe.2021208-15","doi-asserted-by":"publisher","unstructured":"N. Bi, J. Tan, J. Lai, J. H. Lai, C. Y. Suen, High-dimensional supervised feature selection via optimized kernel mutual information, <i>Expert Syst. Appl.<\/i>, <b>108<\/b> (2018), 81-95.","DOI":"10.1016\/j.eswa.2018.04.037"},{"key":"key-10.3934\/mbe.2021208-16","doi-asserted-by":"publisher","unstructured":"F. V. Farahani, A. Ahmadi, M. H. F. Zarandi, Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning, <i>Math. Comput. Simul.<\/i>, <b>149<\/b> (2018), 48-68.","DOI":"10.1016\/j.matcom.2018.02.001"},{"key":"key-10.3934\/mbe.2021208-17","doi-asserted-by":"crossref","unstructured":"I. Ali, G. R. Hart, G. Gunabushanam, Y. Liang, W. Muhammad, B. Nartowt, et al., Lung nodule detection via deep reinforcement learning, <i>Front. Oncol.<\/i>, <b>8<\/b> (2018), 108.","DOI":"10.3389\/fonc.2018.00108"},{"key":"key-10.3934\/mbe.2021208-18","doi-asserted-by":"publisher","unstructured":"C. A. de P. Pinheiro, N. Nedjah, L. de M. Mourelle, Detection and classification of pulmonary nodules using deep learning and swarm intelligence, <i>Multimedia Tools Appl.<\/i>, <b>79<\/b> (2020), 15437-15465.","DOI":"10.1007\/s11042-019-7473-z"},{"key":"key-10.3934\/mbe.2021208-19","doi-asserted-by":"publisher","unstructured":"F. Abdolali, J. Kapur, J. L. Jaremko, M. Noga, A. R. Hareendranathan, K. Punithakumar, Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks, <i>Comput. Biol. Med.<\/i>, <b>122<\/b> (2020), 103871.","DOI":"10.1016\/j.compbiomed.2020.103871"},{"key":"key-10.3934\/mbe.2021208-20","doi-asserted-by":"publisher","unstructured":"M. R. G. Raman, N. Somua, K. Kirthivasan, R. Liscano, V. S. S. Sriram, An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine, <i>Knowl.-Based Syst.<\/i>, <b>134<\/b> (2017), 1-12.","DOI":"10.1016\/j.knosys.2017.07.005"},{"key":"key-10.3934\/mbe.2021208-21","doi-asserted-by":"publisher","unstructured":"A. R. Jordehi, A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems, <i>Neural Comput. Appl.<\/i>, <b>26<\/b> (2015), 827-833.","DOI":"10.1007\/s00521-014-1751-5"},{"key":"key-10.3934\/mbe.2021208-22","doi-asserted-by":"publisher","unstructured":"S. Wu, H. D. Wan, S. K. Shukla, B. Li, Chaos-based improved immune algorithm (CB\u2161A) for resource-constrained project scheduling problems, <i>Expert Syst. Appl.<\/i>, <b>38<\/b> (2011), 3387-3395.","DOI":"10.1016\/j.eswa.2010.08.124"},{"key":"key-10.3934\/mbe.2021208-23","doi-asserted-by":"publisher","unstructured":"H. Liang, F. Kang, Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application, <i>Optik<\/i>, <b>127<\/b> (2016), 3459-3465.","DOI":"10.1016\/j.ijleo.2015.12.122"},{"key":"key-10.3934\/mbe.2021208-24","doi-asserted-by":"publisher","unstructured":"K. Chellapilla, Combining mutation operators in evolutionary programming, <i>IEEE Trans. Evol. Comput.<\/i>, <b>2<\/b> (1998), 91-96.","DOI":"10.1109\/4235.735431"},{"key":"key-10.3934\/mbe.2021208-25","doi-asserted-by":"publisher","unstructured":"Z. Wang, J. Xin, P. Sun, Z. Lin, Y. Yao, X. Gao, Improved lung nodule diagnosis accuracy using lung CT images with uncertain class, <i>Comput. Methods Programs Biomed.<\/i>, <b>162<\/b> (2018), 197-209.","DOI":"10.1016\/j.cmpb.2018.05.028"},{"key":"key-10.3934\/mbe.2021208-26","doi-asserted-by":"publisher","unstructured":"Q. Mao, S. Zhao, Q. Zheng, S. Su, L. Li, X. Zhang, Modified Gaussian models for pulmonary nodule simulation in chest tomosynthesis, <i>J. Med. Imaging Health Inf.<\/i>, <b>8<\/b> (2018), 1718-1725.","DOI":"10.1166\/jmihi.2018.2498"},{"key":"key-10.3934\/mbe.2021208-27","unstructured":"S. G. Armato \u2162, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, et al., Data from LIDC-IDRI, The cancer imaging archive, 2015. Available from: <a href=\"http:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX\" target=\"_blank\">http:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX<\/a>."},{"key":"key-10.3934\/mbe.2021208-28","doi-asserted-by":"crossref","unstructured":"M. Hilts, C. Duzenli, Image filtering for improved dose resolution in CT polymer gel dosimetry, <i>Med. Phys.<\/i>, <b>31<\/b> (2004), 39-49.","DOI":"10.1118\/1.1633106"},{"key":"key-10.3934\/mbe.2021208-29","doi-asserted-by":"publisher","unstructured":"Q. Mao, S. Zhao, T. Gong, Q. Zheng, An effective hybrid windowed Fourier filtering and fuzzy c-mean for pulmonary nodule segmentation, <i>J. Med. Imaging Health Inf.<\/i>, <b>8<\/b> (2018), 72-77.","DOI":"10.1166\/jmihi.2018.2235"},{"key":"key-10.3934\/mbe.2021208-30","doi-asserted-by":"publisher","unstructured":"Q. Mao, S. Zhao, Modified rolling-ball method for pulmonary parenchyma segmentation, <i>J. Med. Imaging Health Inf.<\/i>, <b>10<\/b> (2020), 364-369.","DOI":"10.1166\/jmihi.2020.2882"},{"key":"key-10.3934\/mbe.2021208-31","doi-asserted-by":"crossref","unstructured":"J. k. Liu, H. Jiang, M. Gao, C. He, Y. Wang, P. Wang, et al., An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images, <i>J. Med. Syst.<\/i>, <b>41<\/b> (2017), 30.","DOI":"10.1007\/s10916-016-0669-0"},{"key":"key-10.3934\/mbe.2021208-32","doi-asserted-by":"publisher","unstructured":"X. L. Liu, F. Hou, H. Qin, A. Hao, A CADe system for nodule detection in thoracic CT images based on artificial neural network, <i>Sci. China Inf. Sci.<\/i>, <b>60<\/b> (2017), 072106.","DOI":"10.1007\/s11432-016-9008-0"},{"key":"key-10.3934\/mbe.2021208-33","doi-asserted-by":"publisher","unstructured":"A. Kaya, Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics, <i>Comput. Methods Prog. Biomed<\/i>., <b>166<\/b> (2018), 77-89.","DOI":"10.1016\/j.cmpb.2018.10.009"},{"key":"key-10.3934\/mbe.2021208-34","doi-asserted-by":"crossref","unstructured":"S. A. Khan, S. Hussain, S. Yang, K. Iqbal, Effective and reliable framework for lung nodules detection from CT scan images, <i>Sci. Rep.<\/i>, <b>9<\/b> (2019), 1-14.","DOI":"10.1038\/s41598-019-41510-9"},{"key":"key-10.3934\/mbe.2021208-35","doi-asserted-by":"crossref","unstructured":"G. Zhang, X. Liu, D. Zhu, P. He, L. Liang, Y. Luo, et al., 3D spatial pyramid dilated network for pulmonary nodule classification, <i>Symmetry-Basel<\/i>, <b>10<\/b> (2018), 376.","DOI":"10.3390\/sym10090376"},{"key":"key-10.3934\/mbe.2021208-36","doi-asserted-by":"publisher","unstructured":"H. Jiang, F. Gao, X. Xu, F. Huang, S. Zhu, Attentive and ensemble 3D dual path networks for pulmonary nodules classification, <i>Neurocomputing<\/i>, <b>398<\/b> (2020), 422-430.","DOI":"10.1016\/j.neucom.2019.03.103"}],"container-title":["Mathematical Biosciences and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2021208?viewType=html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T16:02:55Z","timestamp":1699113775000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2021208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.3934\/mbe.2021208","relation":{},"ISSN":["1551-0018"],"issn-type":[{"value":"1551-0018","type":"print"}],"subject":[],"published":{"date-parts":[[2021]]}}}