{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:54:42Z","timestamp":1782849282414,"version":"3.54.5"},"reference-count":56,"publisher":"Walter de Gruyter GmbH","issue":"4","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Skin cancer is a prevalent and potentially life-threatening condition, where early and accurate detection is crucial for effective treatment. Traditional diagnosis, based on visual assessment, faces challenges due to inter-class variability and the similarity between benign and malignant lesions. Although automated diagnostic systems are gaining attention, few offer comprehensive evaluations. This study addresses this gap by systematically assessing such systems and reporting extensive performance metrics.<\/jats:p>\n                  <jats:p>This study contributes to both artificial intelligence and dermatological image analysis by introducing an automated system for multi-class skin lesion classification. The system was evaluated under various configurations, including original and artifact-inpainted images. Three approaches were employed: deep learning, radiomics-based and hybrid. Additionally, to enhance the interpretability, the system generates segmentation masks and attribution maps.<\/jats:p>\n                  <jats:p>Models were evaluated using five-fold cross-validation on the International Skin Imaging Collaboration (ISIC) 2018 Challenge Task 3 training dataset, with final performance assessed on the official test set. The best-performing model achieved a micro accuracy of 79.76\u00b10.73, macro F1-score of 73.62\u00b11.38, precision of 74.80\u00b10.84, and recall of 73.93\u00b12.19, surpassing comparable methods reported in the literature.<\/jats:p>\n                  <jats:p>The contribution to the research community is the release of a novel set of 57630 binary artifact labels for the ISIC-2018 Task 3 dataset, enabling research on the impact of visual artifacts such as hair, rulers, and interface fluids. This contribution is supported by statistical analysis and attribution map assessment, revealing significant differences in artifact occurrence across lesion classes and highlighting the utility of these labels in identifying and mitigating bias in skin lesion classification.<\/jats:p>","DOI":"10.2478\/jaiscr-2026-0016","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T07:16:45Z","timestamp":1782803805000},"page":"315-342","source":"Crossref","is-referenced-by-count":0,"title":["A Holistic Approach to Multi-Modal Skin Lesion Diagnosis Supported by Statistical and Explainability-Based Investigation of Artifacts"],"prefix":"10.2478","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6684-7446","authenticated-orcid":false,"given":"Jakub","family":"Buler","sequence":"first","affiliation":[{"name":"Gda\u0144sk University of Technology , Department of Intelligent Control and Decision Support Systems , Narutowicza 11\/12 , Gda\u0144sk , Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5654-9754","authenticated-orcid":false,"given":"Rafa\u0142","family":"Buler","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Technology , Department of Intelligent Control and Decision Support Systems , Narutowicza 11\/12 , Gda\u0144sk , Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6948-6947","authenticated-orcid":false,"given":"Krystian","family":"Brzozowski","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Technology , Department of Intelligent Control and Decision Support Systems , Narutowicza 11\/12 , Gda\u0144sk , Poland"},{"name":"Medical University of Gda\u0144sk , 2nd Division of Radiology , Marii Sk\u0142odowskiej-Curie 3a , Gda\u0144sk , Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9286-0670","authenticated-orcid":false,"given":"Maria","family":"Ferlin","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Technology , Department of Intelligent Control and Decision Support Systems , Narutowicza 11\/12 , Gda\u0144sk , Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3608-1960","authenticated-orcid":false,"given":"Maciej","family":"Bobowicz","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Technology , Department of Intelligent Control and Decision Support Systems , Narutowicza 11\/12 , Gda\u0144sk , Poland"},{"name":"Medical University of Gda\u0144sk , 2nd Division of Radiology , Marii Sk\u0142odowskiej-Curie 3a , Gda\u0144sk , Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2453-2410","authenticated-orcid":false,"given":"Micha\u0142","family":"Grochowski","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Technology , Department of Intelligent Control and Decision Support Systems , Narutowicza 11\/12 , Gda\u0144sk , Poland"},{"name":"Medical University of Gda\u0144sk , 2nd Division of Radiology , Marii Sk\u0142odowskiej-Curie 3a , Gda\u0144sk , Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_001","unstructured":"International Agency for Research on Cancer, Cancer TODAY (version 1.1), 2024."},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_002","doi-asserted-by":"crossref","unstructured":"U. 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Johr, Dermoscopy: alternative melanocytic algorithms\u2014the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist, Clinics in Dermatology, vol. 20, no. 3, pp. 240\u2013247, 2002.","DOI":"10.1016\/S0738-081X(02)00236-5"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_006","doi-asserted-by":"crossref","unstructured":"J. S. Henning, S. W. Dusza, S. Q. Wang, A. A. Marghoob, H. S. Rabinovitz, D. Polsky, and A. W. Kopf, The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy, Journal of the American Academy of Dermatology, vol. 56, no. 1, pp. 45\u201352, 2007.","DOI":"10.1016\/j.jaad.2006.09.003"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_007","doi-asserted-by":"crossref","unstructured":"P. Tschandl, C. Rosendahl, and H. Kittler, The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Scientific Data, 2018. 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Ellinger, Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification, Computer Methods and Programs in Biomedicine, vol. 193, p. 105475, 2020.","DOI":"10.1016\/j.cmpb.2020.105475"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_024","doi-asserted-by":"crossref","unstructured":"C. Barata, M. E. Celebi, and J. S. Marques, Explainable skin lesion diagnosis using taxonomies, Pattern Recognition, vol. 110, p. 107413, 2021.","DOI":"10.1016\/j.patcog.2020.107413"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_025","doi-asserted-by":"crossref","unstructured":"B. Cassidy, C. Kendrick, A. Brodzicki, J. Jaworek-Korjakowska, and M. H. Yap, Analysis of the ISIC image datasets: Usage, benchmarks and recommendations, Medical Image Analysis, vol. 75, p. 102305, 2022.","DOI":"10.1016\/j.media.2021.102305"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_026","doi-asserted-by":"crossref","unstructured":"C. Barata, V. Rotemberg, N. C. F. Codella, P. 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Fei-Fei, ImageNet: A large-scale hierarchical image database, in 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255, 2009. ISSN: 1063-6919.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_030","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, in Medical Image Computing and Computer-assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241, Springer International Publishing, 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_031","doi-asserted-by":"crossref","unstructured":"Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, UNet++: A Nested U-Net Architecture for Medical Image Segmentation, in Deep Learning in Medical Image Analysis and Multi-modal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, (Cham), pp. 3\u201311, Springer International Publishing, 2018.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_032","doi-asserted-by":"crossref","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818, 2018.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_033","unstructured":"M. Tan and Q. 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Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization, in Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626, 2017.","DOI":"10.1109\/ICCV.2017.74"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_037","doi-asserted-by":"crossref","unstructured":"C. Scapicchio, M. Gabelloni, A. Barucci, D. Cioni, L. Saba, and E. Neri, A deep look into radiomics, La Radiologia Medica, vol. 126, no. 10, pp. 1296\u20131311, 2021.","DOI":"10.1007\/s11547-021-01389-x"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_038","doi-asserted-by":"crossref","unstructured":"A. Zwanenburg, M. Valli\u00e8res, M. A. Abdalah, H. J. W. L. Aerts, V. Andrearczyk, A. Apte, S. Ashrafinia, S. Bakas, R. J. Beukinga, R. Boellaard, M. Bogowicz, L. Boldrini, I. Buvat, G. J. R. Cook, C. Davatzikos, A. Depeursinge, M.-C. Desseroit, N. Dinapoli, C. V. Dinh, S. Echegaray, I. El Naqa, A. Y. Fedorov, R. Gatta, R. J. Gillies, V. Goh, M. G\u00f6tz, M. Guckenberger, S. M. Ha, M. Hatt, F. Isensee, P. Lambin, S. Leger, R. T. Leijenaar, J. Lenkowicz, F. Lippert, A. Losneg\u00e5rd, K. H. Maier-Hein, O. Morin, H. M\u00fcller, S. Napel, C. Nioche, F. Orlhac, S. Pati, E. A. Pfaehler, A. Rahmim, A. U. Rao, J. Scherer, M. M. Siddique, N. M. Sijtsema, J. Socarras Fernandez, R. J. Spezi, Emiliano and, S. Tanadini-Lang, D. Thorwarth, E. G. Troost, T. Upadhaya, V. Valentini, L. V. van Dijk, J. van Griethuysen, F. H. van Velden, P. Whybra, C. Richter, and S. L\u00f6ck, The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping, Radiology, vol. 295, no. 2, pp. 328\u2013338, 2020. Publisher: Radiological Society of North America.","DOI":"10.1148\/radiol.2020191145"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_039","doi-asserted-by":"crossref","unstructured":"S. O. Arik and T. 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Grochowski, Comprehensive Quantitative Evaluation of the Performance and Trustworthiness of Deep Learning Models - Skin Lesion Classification Case Study, in 2025 29th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 345\u2013350, Aug. 2025. ISSN: 2835-2807.","DOI":"10.1109\/MMAR65820.2025.11151064"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_043","doi-asserted-by":"crossref","unstructured":"C. E. Shannon, A mathematical theory of communication, Bell System Technical Journal, vol. 27, no. 3, 1948.","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_044","unstructured":"G. E. Batista, A. L. Bazzan, M. C. Monard, and others, Balancing training data for automated annotation of keywords: a case study., Wob, vol. 4, pp. 10\u201318, 2003."},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_045","doi-asserted-by":"crossref","unstructured":"N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. 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Grochowski, Targeted data augmentation for improving model robustness, International Journal of Applied Mathematics and Computer Science, vol. 35, no. 1, pp. 143\u2013155, 2025.","DOI":"10.61822\/amcs-2025-0011"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_048","unstructured":"N. M. Kinyanjui, T. Odonga, C. Cintas, N. C. Codella, R. Panda, P. Sattigeri, and K. R. Varshney, Estimating skin tone and effects on classification performance in dermatology datasets, arXiv preprint arXiv:1910.13268, 2019."},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_049","doi-asserted-by":"crossref","unstructured":"A. Shahzaib, A. B. Siddiqui, N. Anjum, M. U. Rehman, and N. Ramzan, Hair removal and lesion segmentation of dermoscopic images for classification of skin cancer using deep neural networks, Egyptian Informatics Journal, vol. 32, p. 100844, Dec. 2025.","DOI":"10.1016\/j.eij.2025.100844"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_050","doi-asserted-by":"crossref","unstructured":"T. Lee, V. Ng, R. Gallagher, A. Coldman, and D. McLean, Dullrazor\u00ae: A software approach to hair removal from images, Computers in Biology and Medicine, vol. 27, pp. 533\u2013543, Nov. 1997.","DOI":"10.1016\/S0010-4825(97)00020-6"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_051","doi-asserted-by":"crossref","unstructured":"Q. Abbas, M. E. Celebi, and I. F. Garc\u00eda, Hair removal methods: A comparative study for dermoscopy images, Biomedical Signal Processing and Control, vol. 6, pp. 395\u2013404, Oct. 2011.","DOI":"10.1016\/j.bspc.2011.01.003"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_052","doi-asserted-by":"crossref","unstructured":"P. Bibiloni, M. Gonz\u00e1lez-Hidalgo, and S. Massanet, Skin Hair Removal in Dermoscopic Images Using Soft Color Morphology, in Artificial Intelligence in Medicine (A. ten Teije, C. Popow, J. H. Holmes, and L. Sacchi, eds.), (Cham), pp. 322\u2013326, Springer International Publishing, 2017.","DOI":"10.1007\/978-3-319-59758-4_37"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_053","doi-asserted-by":"crossref","unstructured":"A. Huang, S.-Y. Kwan, W.-Y. Chang, M.-Y. Liu, M.-H. Chi, and G.-S. Chen, A robust hair segmentation and removal approach for clinical images of skin lesions, in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3315\u20133318, July 2013. ISSN: 1558-4615.","DOI":"10.1109\/EMBC.2013.6610250"},{"key":"2026063016021587800_j_jaiscr-2026-0016_ref_054","doi-asserted-by":"crossref","unstructured":"F.-Y. Xie, S.-Y. Qin, Z.-G. Jiang, and R.-S. 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