{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T04:09:25Z","timestamp":1768622965479,"version":"3.49.0"},"reference-count":60,"publisher":"Wiley","issue":"13","license":[{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2022,6,10]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Melanoma is a type of a skin cancer or lesion which has the detrimental ramifications on the human health but with early diagnosis it can be cured easily. The actual identification of skin lesion is very challenging because of factors like a very minute difference between lesion and skin and it is very difficult to differentiate among skin cancer types due to visual comparability. Hence an autonomous system for the diagnosis of true skin cancer type is very useful. In this article, we took the leverage of ensemble learning by combining the features of deep learning architectures with traditional features extraction approaches. For segmentation, we have two pipelines for the feature extraction. We extract the features through traditional split and merge approach as well as from deep learning algorithms of contextual encoding along with the attention mechanism. Later we combine the features of both architectures and predict the segmented region through intersection over union mechanism. After that segmented region is classified into three types of skin lesion using hybrid features of Alex\u2010Net and VGG\u201016 through the transfer learning approach. The evaluation has been performed using the ISIC and PH2 datasets for which achieved segmentation accuracy is 97.8% and 96.7%, respectively. Moreover, hybrid classification network able to attain the 98.2% accuracy.<\/jats:p>","DOI":"10.1002\/cpe.6907","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T07:31:54Z","timestamp":1646379114000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1676-362X","authenticated-orcid":false,"given":"Adil H.","family":"Khan","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia"},{"name":"Electrical Engineering Department Prince Mohammad Bin Fahd University (PMU) Al\u2010Khobar Saudi Arabia"}]},{"given":"Dayang NurFatimah","family":"Awang Iskandar","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia"}]},{"given":"Jawad F.","family":"Al\u2010Asad","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department Prince Mohammad Bin Fahd University (PMU) Al\u2010Khobar Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3579-8708","authenticated-orcid":false,"given":"Hiren","family":"Mewada","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department Prince Mohammad Bin Fahd University (PMU) Al\u2010Khobar Saudi Arabia"}]},{"given":"Muhammad Abid","family":"Sherazi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan"}]}],"member":"311","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21565"},{"issue":"1","key":"e_1_2_8_3_1","first-page":"35","article-title":"Patient knowledge of sunscreen guidelines and frequency of physician counseling: a cross\u2010sectional study","volume":"11","author":"Vasicek BE","year":"2018","journal-title":"J Clin Aesthet Dermatol"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2010-0340"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3889\/oamjms.2018.460"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.7150\/jca.37015"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05070-2_9"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30001-7"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.abd.2019.10.004"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"OliveiraA ArzbergerE MassoneC CarreraC ZalaudekI.Verrucous melanoma simulating melanoacanthoma: dermoscopic reflectance confocal microscopic and high\u2010definition optical coherence tomography presentation of a rare melanoma variant; 2016.","DOI":"10.1111\/ajd.12236"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2004.10.015"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40257-017-0283-z"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.7556\/jaoa.2019.067"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.5772\/intechopen.88065"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2008.08.003"},{"issue":"4","key":"e_1_2_8_16_1","first-page":"2639","article-title":"Expert system for offline clinical guidelines and treatment","volume":"9","author":"Saba T","year":"2012","journal-title":"Life Sci J"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.01.005"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23178"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23301"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12497"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3009276"},{"issue":"1","key":"e_1_2_8_22_1","first-page":"1","article-title":"A multilevel features selection framework for skin lesion classification","volume":"10","author":"Akram T","year":"2020","journal-title":"HCIS"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2013.2283803"},{"key":"e_1_2_8_24_1","doi-asserted-by":"crossref","unstructured":"MasoodA Al\u2010JumailyA.Orientation sensitive fuzzy C means based fast level set evolution for segmentation of histopathological images to detect skin cancer. Proceedings of the International Conference on Hybrid Intelligent; 2018:501\u2010510; Springer Cham.","DOI":"10.1007\/978-3-030-14347-3_49"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101924"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600-0846.2010.00472.x"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.24.1.013007"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.05.027"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1400-8"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.2966163"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18020556"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113196"},{"issue":"1","key":"e_1_2_8_33_1","first-page":"1","article-title":"A comparative study of features selection for skin lesion detection from dermoscopic images","volume":"9","author":"Javed R","year":"2020","journal-title":"Netw Model Anal Health Inf Bioinform"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2841428"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217293"},{"key":"e_1_2_8_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11050811"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.11.034"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105351"},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22414"},{"key":"e_1_2_8_40_1","doi-asserted-by":"crossref","unstructured":"ZhangH DanaK ShiJ et al.Context encoding for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018:7151\u20107160; IEEE.","DOI":"10.1109\/CVPR.2018.00747"},{"key":"e_1_2_8_41_1","doi-asserted-by":"crossref","unstructured":"ChaudhuriD AgrawalA.Split\u2010and\u2010merge procedure for image segmentation using bimodality detection approach; 2010.","DOI":"10.14429\/dsj.60.356"},{"key":"e_1_2_8_42_1","doi-asserted-by":"crossref","unstructured":"DengJ DongW SocherR LiLJ LiK Fei\u2010FeiL.Imagenet: a large\u2010scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition; 2009:248\u2010255; IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_8_43_1","doi-asserted-by":"crossref","unstructured":"HuangX BelongieS.Arbitrary style transfer in real\u2010time with adaptive instance normalization. Proceedings of the IEEE International Conference on Computer Vision; 2017:1501\u20101510; IEEE.","DOI":"10.1109\/ICCV.2017.167"},{"key":"e_1_2_8_44_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS SunJ.Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016:770\u2010778; IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_45_1","doi-asserted-by":"crossref","unstructured":"ZhaoH ShiJ QiX WangX JiaJ.Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017:2881\u20102890.","DOI":"10.1109\/CVPR.2017.660"},{"key":"e_1_2_8_46_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.17035"},{"key":"e_1_2_8_47_1","first-page":"419","article-title":"Ph2: a public database for the analysis of dermoscopic images","author":"Mendonca T","year":"2015","journal-title":"Dermoscopy Image Anal"},{"key":"e_1_2_8_48_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2018.5289"},{"key":"e_1_2_8_49_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2018.5238"},{"key":"e_1_2_8_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105241"},{"key":"e_1_2_8_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103738"},{"key":"e_1_2_8_52_1","unstructured":"WenH.II\u2010FCN for skin lesion analysis towards melanoma detection; 2017. arXiv preprint arXiv:1702.08699."},{"key":"e_1_2_8_53_1","unstructured":"YuanY.Automatic skin lesion segmentation with fully convolutional\u2010deconvolutional networks; 2017. arXiv preprint arXiv:1703.05165."},{"key":"e_1_2_8_54_1","unstructured":"BiL KimJ AhnE FengD.Automatic skin lesion analysis using large\u2010scale dermoscopy images and deep residual networks; 2017. arXiv preprint arXiv:1703.04197."},{"key":"e_1_2_8_55_1","unstructured":"JahanifarM TajeddinNZ GooyaA AslBM.Segmentation of lesions in dermoscopy images using saliency map and contour propagation. arXiv; 2017."},{"key":"e_1_2_8_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.03.025"},{"key":"e_1_2_8_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2016.05.002"},{"key":"e_1_2_8_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.02.018"},{"key":"e_1_2_8_59_1","doi-asserted-by":"publisher","DOI":"10.3906\/elk-2101-133"},{"key":"e_1_2_8_60_1","doi-asserted-by":"publisher","DOI":"10.1111\/exd.13777"},{"key":"e_1_2_8_61_1","doi-asserted-by":"crossref","unstructured":"AlomMZ AspirasT TahaTM AsariVK.Skin cancer segmentation and classification with NABLA\u2010N and inception recurrent residual convolutional networks; 2019. arXiv preprint arXiv:1904.11126.","DOI":"10.1109\/NAECON.2018.8556737"}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.6907","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cpe.6907","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.6907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T11:15:51Z","timestamp":1700306151000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.6907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,3]]},"references-count":60,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,6,10]]}},"alternative-id":["10.1002\/cpe.6907"],"URL":"https:\/\/doi.org\/10.1002\/cpe.6907","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"value":"1532-0626","type":"print"},{"value":"1532-0634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,3]]},"assertion":[{"value":"2021-06-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-14","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-03-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e6907"}}