{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:45:30Z","timestamp":1753922730350,"version":"3.37.3"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11042-022-13666-6","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T04:02:16Z","timestamp":1662523336000},"page":"10575-10594","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Skin lesion detection using an ensemble of deep models: SLDED"],"prefix":"10.1007","volume":"82","author":[{"given":"Ali","family":"Shahsavari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5824-9798","authenticated-orcid":false,"given":"Toktam","family":"Khatibi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sima","family":"Ranjbari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"13666_CR1","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1016\/j.procs.2017.08.226","volume":"112","author":"W Abbes","year":"2017","unstructured":"Abbes W, Sellami D (2017) Automatic skin lesions classification using ontology-based semantic analysis of optical standard images. Procedia Comput Sci 112:2096\u20132105","journal-title":"Procedia Comput Sci"},{"key":"13666_CR2","unstructured":"Agarwal M, Damaraju N, Chaieb S Skin lesion analysis toward melanoma detection"},{"issue":"7","key":"13666_CR3","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/S1470-2045(00)00422-8","volume":"2","author":"G Argenziano","year":"2001","unstructured":"Argenziano G, Soyer HP (2001) Dermoscopy of pigmented skin lesions\u2013a valuable tool for early. Lancet Oncol 2(7):443\u2013449","journal-title":"Lancet Oncol"},{"issue":"12","key":"13666_CR4","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1200\/JCO.2005.05.0864","volume":"24","author":"G Argenziano","year":"2006","unstructured":"Argenziano G et al (2006) Dermoscopy improves accuracy of primary care physicians to triage lesions suggestive of skin cancer. J Clin Oncol 24(12):1877\u20131882","journal-title":"J Clin Oncol"},{"key":"13666_CR5","doi-asserted-by":"crossref","unstructured":"Attia M et al (2017) Skin melanoma segmentation using recurrent and convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE","DOI":"10.1109\/ISBI.2017.7950522"},{"issue":"12","key":"13666_CR6","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13666_CR7","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.patcog.2017.04.023","volume":"69","author":"C Barata","year":"2017","unstructured":"Barata C, Celebi ME, Marques JS (2017) Development of a clinically oriented system for melanoma diagnosis. Pattern Recogn 69:270\u2013285","journal-title":"Pattern Recogn"},{"key":"13666_CR8","doi-asserted-by":"crossref","unstructured":"Baumann LS et al (2018) Safety and efficacy of hydrogen peroxide topical solution, 40%(w\/w), in patients with seborrheic keratoses: results from 2 identical, randomized, double-blind, placebo-controlled, phase 3 studies (A-101-SEBK-301\/302). J Am Acad Dermatol 79(5):869\u2013877","DOI":"10.1016\/j.jaad.2018.05.044"},{"issue":"1","key":"13666_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1\u2013127","journal-title":"Found Trends Mach Learn"},{"key":"13666_CR10","doi-asserted-by":"crossref","unstructured":"Bi L et al (2017) Semi-automatic skin lesion segmentation via fully convolutional networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE","DOI":"10.1109\/ISBI.2017.7950583"},{"key":"13666_CR11","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.ejca.2019.02.005","volume":"111","author":"TJ Brinker","year":"2019","unstructured":"Brinker TJ et al (2019) A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur J Cancer 111:148\u2013154","journal-title":"Eur J Cancer"},{"key":"13666_CR12","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.ejca.2018.12.016","volume":"111","author":"TJ Brinker","year":"2019","unstructured":"Brinker TJ et al (2019) Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. Eur J Cancer 111:30\u201337","journal-title":"Eur J Cancer"},{"key":"13666_CR13","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ejca.2019.05.023","volume":"119","author":"TJ Brinker","year":"2019","unstructured":"Brinker TJ et al (2019) Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer 119:11\u201317","journal-title":"Eur J Cancer"},{"key":"13666_CR14","unstructured":"Burdick J et al (2017) The impact of segmentation on the accuracy and sensitivity of a melanoma classifier based on skin lesion images. In: SIIM 2017 scientific program: Pittsburgh, PA, June 1-June 3, 2017, David L. Lawrence Convention Center"},{"issue":"3","key":"13666_CR15","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1067\/mjd.2000.106518","volume":"43","author":"P Carli","year":"2000","unstructured":"Carli P et al (2000) Preoperative assessment of melanoma thickness by ABCD score of dermatoscopy. J Am Acad Dermatol 43(3):459\u2013466","journal-title":"J Am Acad Dermatol"},{"issue":"6","key":"13666_CR16","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.compmedimag.2007.01.003","volume":"31","author":"ME Celebi","year":"2007","unstructured":"Celebi ME et al (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31(6):362\u2013373","journal-title":"Comput Med Imaging Graph"},{"key":"13666_CR17","unstructured":"center, c.s. Estimated new cases, 2019. Available from: https:\/\/cancerstatisticscenter.cancer.org\/#!\/"},{"issue":"11","key":"13666_CR18","doi-asserted-by":"publisher","first-page":"e76212","DOI":"10.1371\/journal.pone.0076212","volume":"8","author":"W-Y Chang","year":"2013","unstructured":"Chang W-Y et al (2013) Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PLoS ONE 8(11):e76212","journal-title":"PLoS ONE"},{"key":"13666_CR19","doi-asserted-by":"crossref","unstructured":"Codella NC et al (2017) 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). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"13666_CR20","unstructured":"Collaboration, I.S.I. (2020) ISIC archive. Available from: https:\/\/www.isic-archive.com\/#!\/topWithHeader\/wideContentTop\/main"},{"issue":"2","key":"13666_CR21","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1137\/0728030","volume":"28","author":"AR Conn","year":"1991","unstructured":"Conn AR, Gould NI, Toint P (1991) A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds. SIAM J Numer Anal 28(2):545\u2013572","journal-title":"SIAM J Numer Anal"},{"key":"13666_CR22","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ebiom.2019.04.055","volume":"43","author":"A Dascalu","year":"2019","unstructured":"Dascalu A, David E (2019) Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope. EBioMedicine 43:107\u2013113","journal-title":"EBioMedicine"},{"key":"13666_CR23","unstructured":"D\u00edaz IG (2017) Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. arXiv preprint arXiv:1703.01976"},{"issue":"7639","key":"13666_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115","journal-title":"Nature"},{"key":"13666_CR25","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press"},{"key":"13666_CR26","unstructured":"Grichnik JM, Rhodes AR, Sober AJ (2008) Benign neoplasias and hyperplasias of melanocytes. Fitzpatrick\u2019s dermatology in general medicine, 7th edn, pp 1099\u2013103"},{"issue":"8","key":"13666_CR27","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","volume":"29","author":"HA Haenssle","year":"2018","unstructured":"Haenssle HA et al (2018) Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29(8):1836\u20131842","journal-title":"Ann Oncol"},{"issue":"6789","key":"13666_CR28","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1038\/35016072","volume":"405","author":"RH Hahnloser","year":"2000","unstructured":"Hahnloser RH et al (2000) Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789):947","journal-title":"Nature"},{"key":"13666_CR29","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jbi.2018.08.006","volume":"86","author":"B Harangi","year":"2018","unstructured":"Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25\u201332","journal-title":"J Biomed Inform"},{"key":"13666_CR30","doi-asserted-by":"crossref","unstructured":"He K et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.90"},{"key":"13666_CR31","doi-asserted-by":"crossref","unstructured":"He K et al (2017) Mask r-cnn. In:&nbsp;Proceedings of the IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2017.322"},{"key":"13666_CR32","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.ejca.2019.04.021","volume":"115","author":"A Hekler","year":"2019","unstructured":"Hekler A et al (2019) Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur J Cancer 115:79\u201383","journal-title":"Eur J Cancer"},{"key":"13666_CR33","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.ejca.2019.07.019","volume":"120","author":"A Hekler","year":"2019","unstructured":"Hekler A et al (2019) Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer 120:114\u2013121","journal-title":"Eur J Cancer"},{"key":"13666_CR34","doi-asserted-by":"crossref","unstructured":"Hu H et al (2018) CNNAuth: continuous authentication via two-stream convolutional neural networks. In: 2018 IEEE international conference on networking, architecture and storage (NAS). IEEE","DOI":"10.1109\/NAS.2018.8515693"},{"issue":"9","key":"13666_CR35","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.compbiomed.2011.06.010","volume":"41","author":"AG Isasi","year":"2011","unstructured":"Isasi AG, Zapirain BG, Zorrilla AM (2011) Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms. Comput Biol Med 41(9):742\u2013755","journal-title":"Comput Biol Med"},{"key":"13666_CR36","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/j.procs.2015.04.209","volume":"48","author":"S Jain","year":"2015","unstructured":"Jain S, Pise N (2015) Computer aided melanoma skin cancer detection using image processing. Procedia Comput Sci 48:735\u2013740","journal-title":"Procedia Comput Sci"},{"key":"13666_CR37","doi-asserted-by":"crossref","unstructured":"Jaleel JA, Salim S, Aswin R (2013) Computer aided detection of skin cancer. In: 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE","DOI":"10.1109\/ICCPCT.2013.6528879"},{"issue":"5","key":"13666_CR38","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1109\/TMI.2016.2532122","volume":"35","author":"M Kallenberg","year":"2016","unstructured":"Kallenberg M et al (2016) Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 35(5):1322\u20131331","journal-title":"IEEE Trans Med Imaging"},{"key":"13666_CR39","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization.&nbsp;arXiv preprint arXiv:1412.6980"},{"key":"13666_CR40","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In:&nbsp;Advances in neural information processing systems"},{"key":"13666_CR41","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.trecan.2019.02.002","volume":"5","author":"AB Levine","year":"2019","unstructured":"Levine AB et al (2019) Rise of the machines: advances in deep learning for cancer diagnosis. Trends Cancer 5:157\u2013169","journal-title":"Trends Cancer"},{"issue":"1","key":"13666_CR42","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/JIOT.2018.2851185","volume":"6","author":"Y Li","year":"2018","unstructured":"Li Y, Hu H, Zhou G (2018) Using data augmentation in continuous authentication on smartphones. IEEE Internet Things J 6(1):628\u2013640","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"13666_CR43","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/MIC.2020.2971447","volume":"24","author":"Y Li","year":"2020","unstructured":"Li Y et al (2020) Using feature fusion strategies in continuous authentication on smartphones. IEEE Internet Comput 24(2):49\u201356","journal-title":"IEEE Internet Comput"},{"key":"13666_CR44","unstructured":"Lopez AR et al (2017) Skin lesion classification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED international conference on biomedical engineering (BioMed). IEEE"},{"key":"13666_CR45","doi-asserted-by":"crossref","unstructured":"Majtner T, Yildirim-Yayilgan S, Hardeberg JY (2016) Combining deep learning and hand-crafted features for skin lesion classification. In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE","DOI":"10.1109\/IPTA.2016.7821017"},{"issue":"1","key":"13666_CR46","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1017\/S1351324909005129","volume":"16","author":"C Manning","year":"2010","unstructured":"Manning C, Raghavan P, Sch\u00fctze H (2010) Introduction to information retrieval. Nat Lang Eng 16(1):100\u2013103","journal-title":"Nat Lang Eng"},{"issue":"10083","key":"13666_CR47","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.1016\/S0140-6736(17)31285-0","volume":"389","author":"VJ Mar","year":"2017","unstructured":"Mar VJ, Scolyer RA, Long GV (2017) Computer-assisted diagnosis for skin cancer: have we been outsmarted? Lancet 389(10083):1962\u20131964","journal-title":"Lancet"},{"key":"13666_CR48","doi-asserted-by":"crossref","unstructured":"Marchetti MA et al (2018) Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78(2):270-277. e1","DOI":"10.1016\/j.jaad.2017.08.016"},{"key":"13666_CR49","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ejca.2019.06.013","volume":"119","author":"RC Maron","year":"2019","unstructured":"Maron RC et al (2019) Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer 119:57\u201365","journal-title":"Eur J Cancer"},{"key":"13666_CR50","unstructured":"Matsunaga K et al (2017) Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble. arXiv preprint arXiv:1703.03108"},{"issue":"9321","key":"13666_CR51","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1016\/S0140-6736(02)08741-X","volume":"359","author":"M Megahed","year":"2002","unstructured":"Megahed M et al (2002) Reliability of diagnosis of melanoma in situ. Lancet 359(9321):1921\u20131922","journal-title":"Lancet"},{"key":"13666_CR52","unstructured":"Mendonca T et al (2015) PH2: A public database for the analysis of dermoscopic images. In:&nbsp;Dermoscopy image analysis. CRC Press"},{"key":"13666_CR53","unstructured":"Menegola A et al (2017) RECOD titans at ISIC challenge 2017. arXiv preprint arXiv:1703.04819"},{"key":"13666_CR54","doi-asserted-by":"crossref","unstructured":"Mirzaalian-Dastjerdi H et al (2018) Detecting and measuring surface area of skin lesions, in Bildverarbeitung f\u00fcr die Medizin 2018. Springer, pp 29\u201334","DOI":"10.1007\/978-3-662-56537-7_20"},{"issue":"1","key":"13666_CR55","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/0895-6111(89)90076-1","volume":"13","author":"RH Moss","year":"1989","unstructured":"Moss RH et al (1989) Skin cancer recognition by computer vision. Comput Med Imaging Graph 13(1):31\u201336","journal-title":"Comput Med Imaging Graph"},{"issue":"7","key":"13666_CR56","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1016\/j.jid.2019.01.008","volume":"139","author":"SA Mueller","year":"2019","unstructured":"Mueller SA et al (2019) Mutational patterns in metastatic cutaneous squamous cell carcinoma. J Invest Dermatol 139(7):1449-1458.e1","journal-title":"J Invest Dermatol"},{"key":"13666_CR57","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ijmedinf.2019.01.005","volume":"124","author":"N Nida","year":"2019","unstructured":"Nida N et al (2019) Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int J Med Informatics 124:37\u201348","journal-title":"Int J Med Informatics"},{"key":"13666_CR58","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.engappai.2018.04.028","volume":"73","author":"E Okur","year":"2018","unstructured":"Okur E, Turkan M (2018) A survey on automated melanoma detection. Eng Appl Artif Intell 73:50\u201367","journal-title":"Eng Appl Artif Intell"},{"key":"13666_CR59","doi-asserted-by":"crossref","unstructured":"Renzi M et al (2019) Management of skin cancer in the elderly. Dermatol Clin 37(3):279\u2013286","DOI":"10.1016\/j.det.2019.02.003"},{"key":"13666_CR60","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neunet.2019.04.025","volume":"116","author":"M Sar\u0131g\u00fcl","year":"2019","unstructured":"Sar\u0131g\u00fcl M, Avci BMOM (2019) Differential convolutional neural network. Neural Netw 116:279\u2013287","journal-title":"Neural Netw"},{"issue":"4","key":"13666_CR61","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s12293-014-0144-8","volume":"6","author":"G Schaefer","year":"2014","unstructured":"Schaefer G et al (2014) An ensemble classification approach for melanoma diagnosis. Memetic Comput 6(4):233\u2013240","journal-title":"Memetic Comput"},{"key":"13666_CR62","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.eswa.2018.10.029","volume":"118","author":"A Soudani","year":"2019","unstructured":"Soudani A, Barhoumi W (2019) An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction. Expert Syst Appl 118:400\u2013410","journal-title":"Expert Syst Appl"},{"key":"13666_CR63","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.clindermatol.2019.06.004","volume":"37","author":"S Sreekantaswamy","year":"2019","unstructured":"Sreekantaswamy S et al (2019) Aging and the treatment of basal cell carcinoma. Clin Dermatol 37:373\u2013378","journal-title":"Clin Dermatol"},{"key":"13666_CR64","doi-asserted-by":"crossref","unstructured":"Stoecker WV, Moss RH (1992) Digital imaging in dermatology. Elsevier","DOI":"10.1016\/0895-6111(92)90068-K"},{"issue":"3","key":"13666_CR65","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1600-0846.2005.00117.x","volume":"11","author":"WV Stoecker","year":"2005","unstructured":"Stoecker WV et al (2005) Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color. Skin Res Technol 11(3):179\u2013184","journal-title":"Skin Res Technol"},{"key":"13666_CR66","doi-asserted-by":"crossref","unstructured":"Szegedy C et al (2015) Going deeper with convolutions. In:&nbsp;Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"2","key":"13666_CR67","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings JR et al (2013) Selective search for object recognition. Int J Comput Vision 104(2):154\u2013171","journal-title":"Int J Comput Vision"},{"key":"13666_CR68","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.patrec.2017.11.005","volume":"139","author":"CN Vasconcelos","year":"2017","unstructured":"Vasconcelos CN, Vasconcelos BN (2017) Experiments using deep learning for dermoscopy image analysis. Pattern Recognit Lett 139:95\u2013103","journal-title":"Pattern Recognit Lett"},{"key":"13666_CR69","doi-asserted-by":"crossref","unstructured":"Xie S et al (2017) Aggregated residual transformations for deep neural networks. In:&nbsp;Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2017.634"},{"issue":"4","key":"13666_CR70","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","volume":"36","author":"L Yu","year":"2016","unstructured":"Yu L et al (2016) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 36(4):994\u20131004","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13666-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13666-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13666-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T16:30:49Z","timestamp":1677774649000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13666-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,7]]},"references-count":70,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["13666"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13666-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,9,7]]},"assertion":[{"value":"21 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}