{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T07:45:58Z","timestamp":1791272758960,"version":"4.1.0"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007751","name":"AGH University of Science and Technology","doi-asserted-by":"publisher","award":["Excellence initiative \u2013 research university"],"award-info":[{"award-number":["Excellence initiative \u2013 research university"]}],"id":[{"id":"10.13039\/501100007751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/N02700\/1"],"award-info":[{"award-number":["EP\/N02700\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Over the past few decades, different clinical diagnostic algorithms have been proposed to diagnose malignant melanoma in its early stages. Furthermore, the detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. However, in all these approaches, the researchers do not take into account the origin of the skin lesion. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites, including the face, trunk and extremities, and acral lesions. In this study, we take advantage of pretrained networks, including VGG19, ResNet50, Xception, DenseNet121, and EfficientNetB0, to calculate the features with an adjusted and densely connected classifier. Furthermore, we perform in depth analysis on database, architecture, and result regarding the effectiveness of the proposed framework. Experiments confirm the ability of the developed algorithms to classify skin lesions into the most important anatomical sites with 91.45% overall accuracy for the EfficientNetB0 architecture, which is a state-of-the-art result in this domain.<\/jats:p>","DOI":"10.3390\/cancers13236048","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:43:22Z","timestamp":1638323002000},"page":"6048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Interpretability of a Deep Learning Based Approach for the Classification of Skin Lesions into Main Anatomic Body Sites"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0146-8652","authenticated-orcid":false,"given":"Joanna","family":"Jaworek-Korjakowska","sequence":"first","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7713-526X","authenticated-orcid":false,"given":"Andrzej","family":"Brodzicki","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3741-8120","authenticated-orcid":false,"given":"Bill","family":"Cassidy","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3623-6598","authenticated-orcid":false,"given":"Connah","family":"Kendrick","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7681-4287","authenticated-orcid":false,"given":"Moi Hoon","family":"Yap","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"ref_1","unstructured":"Society, A.C., and American Cancer Society (2021, October 14). Cancer Statistics Center. Available online: https:\/\/cancerstatisticscenter.cancer.org\/#!\/."},{"key":"ref_2","unstructured":"Argenziano, G., Soyer, P., De Giorgi, V., Piccolo, D., Carli, P., Delfino, M., Ferrari, A., Hofmann-Wellenhof, R., Massi, D., and Mazzocchetti, G. (2000). Interactive Atlas of Dermoscopy, Edra Medical Publishing & New Media."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1001\/archderm.134.12.1563","article-title":"Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis","volume":"134","author":"Argenziano","year":"1998","journal-title":"Arch. Dermatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1067\/mjd.2003.281","article-title":"Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet","volume":"48","author":"Argenziano","year":"2003","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/S0190-9622(94)70061-3","article-title":"The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions","volume":"30","author":"Nachbar","year":"1994","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, C., Yang, S., Kim, W., Jung, J., Chung, K.Y., Lee, S.W., and Oh, B. (2018). Acral melanoma detection using a convolutional neural network for dermoscopy images. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196621"},{"key":"ref_7","unstructured":"Le, D.N., Le, H.X., Ngo, L., and Ngo, H.T. (2020). Transfer learning with class-weighted and focal loss function for automatic skin cancer classification. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ejca.2019.11.020","article-title":"Melanoma recognition by a deep learning convolutional neural network\u2014Performance in different melanoma subtypes and localisations","volume":"127","author":"Winkler","year":"2020","journal-title":"Eur. J. Cancer"},{"key":"ref_9","unstructured":"GmbH, F.S. (2021, October 14). FotoFinder Moleanalyzer Pro & AI; 2013 FotoFinder Moleanalyzer Pro. Available online: https:\/\/www.fotofinder.de\/en\/technology\/artificial-intelligence."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1001\/jamadermatol.2019.3807","article-title":"Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network","volume":"15","author":"Han","year":"2020","journal-title":"JAMA Dermatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.adengl.2019.09.003","article-title":"Machine Learning in Melanoma Diagnosis. Limitations about to be Overcome","volume":"111","author":"Jofre","year":"2020","journal-title":"Actas Dermo-Sifiliogr\u00e1ficas (Engl. Ed.)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"180161","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gutman, D.A., Codella, N.C.F., Celebi, M.E., Helba, B., Marchetti, M.A., Mishra, N.K., and Halpern, A.C. (2018, January 4\u20137). 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). Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_14","unstructured":"Combalia, M., Codella, N.C.F., Rotemberg, V., Helba, B., Vilaplana, V., Reiter, O., Halpern, A.C., Puig, S., and Malvehy, J. (2019). BCN20000: Dermoscopic Lesions in the Wild. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: Uniform Manifold Approximation and Projection","volume":"3","author":"McInnes","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_16","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat.\u2014Theory Methods"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A Cluster Separation Measure","volume":"PAMI-1","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_23","unstructured":"Tan, M., and Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_24","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","unstructured":"Kingma, D.P., and Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1038\/s41591-020-0942-0","article-title":"Human\u2013computer collaboration for skin cancer recognition","volume":"26","author":"Tschandl","year":"2020","journal-title":"Nat. Med."},{"key":"ref_29","unstructured":"Chollet, F. (2021, October 14). Keras. Available online: https:\/\/keras.io\/."},{"key":"ref_30","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/13\/23\/6048\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:38:18Z","timestamp":1760168298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/13\/23\/6048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,1]]},"references-count":30,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["cancers13236048"],"URL":"https:\/\/doi.org\/10.3390\/cancers13236048","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,1]]}}}