{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T11:42:01Z","timestamp":1768909321336,"version":"3.49.0"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Sultan Qaboos University, Muscat, Oman, through the Internal Research","award":["IG\/ENG\/ECED\/24\/02"],"award-info":[{"award-number":["IG\/ENG\/ECED\/24\/02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2025.3641868","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T18:41:50Z","timestamp":1765219310000},"page":"6287-6309","source":"Crossref","is-referenced-by-count":0,"title":["SynthraXCoreNet: An Interpretable, Well-Calibrated Six-CNN Ensemble for Dermoscopic Skin-Lesion Classification"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3320-3897","authenticated-orcid":false,"given":"Muhammad","family":"Adeel Ajmal Khan","sequence":"first","affiliation":[{"name":"Department of Computing, Institute of Space Technology, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2890-3661","authenticated-orcid":false,"given":"Madiha","family":"Tahir","sequence":"additional","affiliation":[{"name":"Department of Computing, Institute of Space Technology, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5979-4448","authenticated-orcid":false,"given":"Syed","family":"Ali Irtaza","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0660-2761","authenticated-orcid":false,"given":"Muhammad Rizwan","family":"Mughal","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Oman"}]},{"given":"Muhammad","family":"Nadeem Yousaf","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Munster Technological University (MTU), Cork, Ireland"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298188"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref5","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"He"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref7","article-title":"Learning transferable architectures for scalable image recognition","author":"Zoph","year":"2017","journal-title":"arXiv:1707.07012"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1201\/b12207"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref12","article-title":"Grad-CAM++: Improved visual explanations for deep convolutional networks","author":"Chattopadhyay","year":"2017","journal-title":"arXiv:1710.11063"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3089943"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"ref15","volume-title":"Score-Cam: Score-Weighted Visual Explanations for CNNs (Official Implementation)","author":"Tabayashi","year":"2020"},{"key":"ref16","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2013","journal-title":"arXiv:1312.6034"},{"key":"ref17","article-title":"SmoothGrad: Removing noise by adding noise","author":"Smilkov","year":"2017","journal-title":"arXiv:1706.03825"},{"key":"ref18","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Sundararajan"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2017.10.011"},{"key":"ref21","first-page":"1","article-title":"Evaluating the visualization of what a deep neural network has learned","volume-title":"Proc. IEEE Int. Conf. Comput. Vis. (ICCV)","author":"Samek"},{"key":"ref22","first-page":"10967","article-title":"On the (in)fidelity and sensitivity of explanations","volume-title":"Proc. NeurIPS","author":"Yeh"},{"key":"ref23","first-page":"1383","article-title":"Concise explanations of neural networks using adversarial training","volume-title":"Proc. 37th Int. Conf. Mach. Learn. (ICML)","volume":"1","author":"Chalasani"},{"key":"ref24","article-title":"On quantitative aspects of model interpretability","author":"Nguyen","year":"2020","journal-title":"arXiv:2007.07584"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/417"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_14"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"key":"ref28","volume-title":"ISIC 2019: Skin Lesion Analysis Towards Melanoma Detection","year":"2019"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-03387-w"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-04104-3"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3076533"},{"issue":"4","key":"ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuri.2021.100034","article-title":"Multiclass skin cancer classification using EfficientNets\u2014A first step towards preventing skin cancer","volume":"2","author":"Ali","year":"2022","journal-title":"Neurosci. Informat."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3196911"},{"key":"ref34","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Lundberg"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3217217"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3319087"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3360215"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3204646"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3192444"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3149824"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref42","article-title":"Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data","author":"Gessert","year":"2019","journal-title":"arXiv:1910.03910"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neunet.2023.01.022","article-title":"Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare","volume":"160","author":"Maqsood","year":"2023","journal-title":"Neural Netw."},{"issue":"2","key":"ref44","doi-asserted-by":"crossref","first-page":"438","DOI":"10.3390\/electronics12020438","article-title":"A skin disease classification model based on DenseNet and ConvNeXt fusion","volume":"12","author":"Wei","year":"2023","journal-title":"Electronics"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3324042"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-22644-9"},{"issue":"8","key":"ref47","doi-asserted-by":"crossref","first-page":"2852","DOI":"10.3390\/s21082852","article-title":"Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM","volume":"21","author":"Srinivasu","year":"2021","journal-title":"Sensors"},{"key":"ref48","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107166","article-title":"Skin lesion classification using CNNs with grouping of multi-scale attention and class-specific loss weighting","volume":"226","author":"Qian","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref49","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103997","article-title":"Enhanced deep bottleneck transformer model for skin lesion classification","volume":"78","author":"Nakai","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ymeth.2021.02.013","article-title":"A hierarchical three-step superpixels and deep learning framework for skin lesion classification","volume":"202","author":"Afza","year":"2022","journal-title":"Methods"},{"key":"ref51","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106148","article-title":"S2C-DeLeNet: A parameter transfer based segmentation-classification integration for detecting skin cancer lesions from dermoscopic images","volume":"150","author":"Alam","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref52","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103729","article-title":"Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory","volume":"76","author":"Elashiri","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07234-1"},{"key":"ref54","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119064","article-title":"Wavelet transform based deep residual neural network and ReLU based extreme learning machine for skin lesion classification","volume":"213","author":"Alenezi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref55","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2023.113409","article-title":"A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks","volume":"170","author":"Alhudhaif","year":"2023","journal-title":"Chaos, Solitons Fractals"},{"key":"ref56","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106666","article-title":"Hierarchy-aware contrastive learning with late fusion for skin lesion classification","volume":"216","author":"Hsu","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref57","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116671","article-title":"Multi-class skin lesion classification using prism{-} and segmentation-based fractal signatures","volume":"197","author":"Camacho-Guti\u00e9rrez","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref58","doi-asserted-by":"crossref","DOI":"10.1016\/j.tice.2021.101701","article-title":"Multi-features extraction based on deep learning for skin lesion classification","volume":"74","author":"Benyahia","year":"2022","journal-title":"Tissue Cell"},{"issue":"22","key":"ref59","doi-asserted-by":"crossref","first-page":"5716","DOI":"10.3390\/cancers14225716","article-title":"Integrated design of optimized weighted deep feature fusion strategies for skin lesion image classification","volume":"14","author":"Mohanty","year":"2022","journal-title":"Cancers"},{"issue":"5","key":"ref60","doi-asserted-by":"crossref","first-page":"e330","DOI":"10.1016\/S2589-7500(22)00021-8","article-title":"Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: The 2019 ISIC grand challenge","volume":"4","author":"Combalia","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref61","first-page":"389","article-title":"Artificial intelligence in dermatology: Current applications and future directions","volume-title":"Current Oncol. Rep.","volume":"25","author":"Raju","year":"2023"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.2307\/2276774"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1009213286"},{"key":"ref64","article-title":"Towards a rigorous science of interpretable machine learning","author":"Doshi-Velez","year":"2017","journal-title":"arXiv:1702.08608"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2599820"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2017.10.011"},{"key":"ref67","article-title":"On the robustness of interpretability methods","author":"Alvarez-Melis","year":"2018","journal-title":"arXiv:1806.08049"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2009.2027527"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-0526(199609\/10)2:1<44::AID-CPLX10>3.3.CO;2-P"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_14"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11284879.pdf?arnumber=11284879","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T20:56:08Z","timestamp":1768856168000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11284879\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3641868","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}