{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:34:55Z","timestamp":1791567295250,"version":"4.3.4"},"reference-count":83,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:00:00Z","timestamp":1741737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"French Agence Nationale de Recherche (ANR)","award":["ANR-21-CE45-0025-01"],"award-info":[{"award-number":["ANR-21-CE45-0025-01"]}]},{"name":"University of Franche Comte","award":["ANR-21-CE45-0025-01"],"award-info":[{"award-number":["ANR-21-CE45-0025-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Misdiagnosing skin disorders leads to the administration of wrong treatments, sometimes with life-impacting consequences. Deep learning algorithms are becoming more and more used for diagnosis. While many skin cancer\/lesion image classification studies focus on datasets containing dermatoscopic images and do not include keloid images, in this study, we focus on diagnosing keloid disorders amongst other skin lesions and combine two publicly available datasets containing non-dermatoscopic images: one dataset with keloid images and one with images of other various benign and malignant skin lesions (melanoma, basal cell carcinoma, squamous cell carcinoma, actinic keratosis, seborrheic keratosis, and nevus). Methods: Different Convolution Neural Network (CNN) models are used to classify these disorders as either malignant or benign, to differentiate keloids amongst different benign skin disorders, and furthermore to differentiate keloids among other similar-looking malignant lesions. To this end, we use the transfer learning technique applied to nine different base models: the VGG16, MobileNet, InceptionV3, DenseNet121, EfficientNetB0, Xception, InceptionRNV2, EfficientNetV2L, and NASNetLarge. We explore and compare the results of these models using performance metrics such as accuracy, precision, recall, F1score, and AUC-ROC. Results: We show that the VGG16 model (after fine-tuning) performs the best in classifying keloid images among other benign and malignant skin lesion images, with the following keloid class performance: an accuracy of 0.985, precision of 1.0, recall of 0.857, F1 score of 0.922 and AUC-ROC value of 0.996. VGG16 also has the best overall average performance (over all classes) in terms of the AUC-ROC and the other performance metrics. Using this model, we further attempt to predict the identification of three new non-dermatoscopic anonymised clinical images, classifying them as either malignant, benign, or keloid, and in the process, we identify some issues related to the collection and processing of such images. Finally, we also show that the DenseNet121 model has the best performance when differentiating keloids from other malignant disorders that have similar clinical presentations. Conclusions: The study emphasised the potential use of deep learning algorithms (and their drawbacks), to identify and classify benign skin disorders such as keloids, which are not usually investigated via these approaches (as opposed to cancers), mainly due to lack of available data.<\/jats:p>","DOI":"10.3390\/diagnostics15060710","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T10:45:06Z","timestamp":1741776306000},"page":"710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning Approaches for the Classification of Keloid Images in the Context of Malignant and Benign Skin Disorders"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9444-3944","authenticated-orcid":false,"given":"Olusegun Ekundayo","family":"Adebayo","sequence":"first","affiliation":[{"name":"Laboratoire de Math\u00e9matiques de Besan\u00e7on, Universit\u00e9 Marie et Louis Pasteur, F-25000 Besan\u00e7on, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-136X","authenticated-orcid":false,"given":"Brice","family":"Chatelain","sequence":"additional","affiliation":[{"name":"Service de Chirurgie Maxillo-Faciale, Stomatologie et Odontologie Hospitali\u00e8re, CHU Besan\u00e7on, F-25000 Besan\u00e7on, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dumitru","family":"Trucu","sequence":"additional","affiliation":[{"name":"Division of Mathematics, University of Dundee, Dundee DD1 4HN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9726-1498","authenticated-orcid":false,"given":"Raluca","family":"Eftimie","sequence":"additional","affiliation":[{"name":"Laboratoire de Math\u00e9matiques de Besan\u00e7on, Universit\u00e9 Marie et Louis Pasteur, F-25000 Besan\u00e7on, France"},{"name":"Division of Mathematics, University of Dundee, Dundee DD1 4HN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.4103\/ijd.ijd_783_21","article-title":"Appearances can be deceptive: An intriguing case of a keloidal mass","volume":"67","author":"Behera","year":"2022","journal-title":"Indian J. Dermatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/S0190-9622(99)70435-4","article-title":"The misdiagnosis of malignant melanoma","volume":"40","author":"Bason","year":"1999","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"86","DOI":"10.7181\/acfs.2016.17.2.86","article-title":"Malignant skin tumor misdiagnosed as a benign skin lesion","volume":"17","author":"Hwang","year":"2016","journal-title":"Arch. Craniofac. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e9047","DOI":"10.1097\/MD.0000000000009047","article-title":"A melanotic malignant melanoma presenting as a keloid. A case report","volume":"96","author":"Jia","year":"2017","journal-title":"Medicine"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"bcr2014203600","DOI":"10.1136\/bcr-2014-203600","article-title":"An important case","volume":"2014","author":"Nicholas","year":"2014","journal-title":"BMJ Case Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e4332","DOI":"10.1097\/MD.0000000000004332","article-title":"Initial misdiagnosis of melanoma located on the foot is associated with poorer prognosis","volume":"95","author":"Sondermann","year":"2016","journal-title":"Medicine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"74","DOI":"10.5455\/medarh.2018.72.74-75","article-title":"A rare case of misdiagnosis: Recurrence of dermatofibrosarcoma protuberans that was treated surgically as keloid","volume":"72","author":"Ucak","year":"2018","journal-title":"Med. Arch."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1111\/iwj.13099","article-title":"On the diagnosis and treatment of cutaneous and soft tissue tumours misdiagnosed as scars: Lessons from four cases","volume":"16","author":"Wang","year":"2019","journal-title":"Int. Wound J."},{"key":"ref_9","first-page":"e27285","article-title":"Cutaneous Metastasis of Lung Adenocarcinoma: Initially Mimicking and Misdiagnosed as Keloids","volume":"14","author":"Newcomer","year":"2022","journal-title":"Cureus"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1097\/00006534-199910000-00031","article-title":"On the nature of hypertrophic scars and keloids: A review","volume":"104","author":"Niessen","year":"1999","journal-title":"Plast. Reconstr. Surg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1038\/sj.jid.5701107","article-title":"Human skin keloid fibroblasts display bioenergetics of cancer cells","volume":"128","author":"Vincent","year":"2008","journal-title":"J. Investig. Dermatol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113","DOI":"10.2119\/molmed.2009.00153","article-title":"Hypertrophic scarring and keloids: Pathomechanisms and current and emerging treatment strategies","volume":"17","author":"Gauglitz","year":"2011","journal-title":"Mol. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/j.1743-6109.2006.00135.x","article-title":"Gene expression patterns in isolated keloid fibroblasts","volume":"14","author":"Satish","year":"2006","journal-title":"Wound Repair Regen."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Eftimie, R., Rollin, G., Adebayo, O., Urcun, S., and Bordas, S.P.A. (2023). Modelling keloids dynamics: A brief review and new mathematical perspectives. Bull. Math. Biol., 85.","DOI":"10.1007\/s11538-023-01222-8"},{"key":"ref_15","first-page":"154","article-title":"Cutaneous leiomyosarcoma misdiagnosed as a keloid: A case report with dermatoscopy","volume":"57","author":"Bostanci","year":"2023","journal-title":"Turkderm.-Turk. Arch. Dermatol. Venereol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"82","DOI":"10.5935\/2177-1235.2016RBCP0012","article-title":"Dermatofibrosarcoma protuberans misdiagnosed as keloid and treated with triamcinolone acetonid","volume":"31","author":"Portugal","year":"2016","journal-title":"Rev. Bras. Cir. Plast."},{"key":"ref_17","first-page":"466","article-title":"Challenging Diagnosis of a Rare Case of Spontaneous Keloid Scar","volume":"5","author":"Tiong","year":"2014","journal-title":"J. Med. Cases"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"274","DOI":"10.23736\/S2784-8671.20.06784-X","article-title":"Dermatoscopy of melanoma according to type, anatomic site and stage","volume":"156","author":"Lallas","year":"2021","journal-title":"Ital. J. Dermatol. Venereol."},{"key":"ref_19","first-page":"135","article-title":"Dermoscopic criteria and basal cell carcinoma","volume":"147","author":"Puig","year":"2012","journal-title":"G. Ital. Dermatol. Venereol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sgouros, D., Theofili, M., Zafeiropoulou, T., Lallas, A., Apalla, Z., Zaras, A., Liopyris, K., Pappa, G., Polychronaki, E., and Kousta, F. (2023). Dermoscopy of Actinic Keratosis: Is There a True Differentiation between Non-Pigmented and Pigmented Lesions?. J. Clin. Med., 12.","DOI":"10.3390\/jcm12031063"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fee, J.A., McGrady, F.P., and Hart, N.D. (2022). Dermoscopy use in primary care: A qualitative study with general practitioners. BMC Primary Care, 23.","DOI":"10.1186\/s12875-022-01653-7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1111\/jdv.14129","article-title":"The impact of dermoscopy on melanoma detection in the practice of dermatologists in Europe: Results of a pan-European survey","volume":"31","author":"Forsea","year":"2017","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1001\/archderm.141.2.155","article-title":"Limitations of dermoscopy in the recognition of melanoma","volume":"141","author":"Skvara","year":"2005","journal-title":"Arch. Dermatol."},{"key":"ref_24","unstructured":"Chollet, F. (2018). Deep Learning with Python, Manning Publications."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jo, T., Nho, K., and Saykin, A. (2019). Deep Learning in Alzheimer\u2019s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data. Front. Aging Neurosci., 11.","DOI":"10.3389\/fnagi.2019.00220"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pacheco, A.G.C., and Krohling, R.A. (2020). The impact of patient clinical information on automated skin cancer detection. Comput. Biol. Med., 116.","DOI":"10.1016\/j.compbiomed.2019.103545"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","article-title":"GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification","volume":"321","author":"Diamant","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Thirumalaisamy, S., Thangavilou, K., Rajadurai, H., Saidani, O., Alturki, N., Mathivanan, S.K., Jayagopal, P., and Gochhait, S. (2023). Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm. Diagnostics, 13.","DOI":"10.3390\/diagnostics13182925"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"535","DOI":"10.26415\/2572-004X-vol4iss2p535-544","article-title":"Breast cancer classification using machine learning techniques: A comparative study","volume":"4","author":"Houfani","year":"2020","journal-title":"Med. Technol. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"535","DOI":"10.3389\/fonc.2022.931141","article-title":"SkinNet-16: A deep learning approach to identify benign and malignant skin lesions","volume":"12","author":"Ghosh","year":"2022","journal-title":"Front. Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kim, J., Oh, I., Lee, Y.N., Kim, J., and Lee, H.J. (2023). Predicting the severity of postoperative scars using artificial intelligence based on images and clinical data. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-40395-z"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, S., Wang, H., Xiao, Y., Zhang, M., Yu, N., Zeng, A., and Wang, X. (2022). A Workflow for Computer-Aided Evaluation of Keloid Based on Laser Speckle Contrast Imaging and Deep Learning. J. Pers. Med., 12.","DOI":"10.3390\/jpm12060981"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pacheco, A.G.C., Lima, G.R., Salom\u00e3o, A.S., Krohling, B., Biral, I.P., Angelo, G.G., Alves, F.C.R., Esgario, J.G.M., Simora, A.C., and Castro, P.B.C. (2020). PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Mendeley Data, V1.","DOI":"10.1016\/j.dib.2020.106221"},{"key":"ref_35","unstructured":"Kaggle (2021, November 01). Skin Cancer: Malignant vs. Benign. Available online: https:\/\/www.kaggle.com\/datasets\/fanconic\/skin-cancermalignant-vs-benign."},{"key":"ref_36","unstructured":"(2022, June 05). Skin Cancer MNIST: HAM10000. Available online: https:\/\/www.kaggle.com\/datasets\/kmader\/skin-cancer-mnist-ham10000?fbclid=IwAR0ZHT4XR1ZBC8fHdPtVkQRlOF0XQIyJNSiG1rohE4rYb7K8vkKx9eVpSjg."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104","DOI":"10.54254\/2755-2721\/5\/20230542","article-title":"Convolutional neural network and vision transformer for image classification","volume":"5","author":"Lu","year":"2023","journal-title":"Appl. Comput. Eng."},{"key":"ref_38","first-page":"10s","article-title":"Transformers in Vision: A Survey","volume":"54","author":"Khan","year":"2022","journal-title":"Assoc. Comput. Mach."},{"key":"ref_39","unstructured":"Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., and Beyer, L. (2022). How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.dsi.2018.04.007","article-title":"Basal cell carcinoma presenting as an excoriated cicatricial plaque: A case report","volume":"36","author":"Acar","year":"2018","journal-title":"Dermatol. Sin."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1097\/DAD.0b013e3181469195","article-title":"Keloidal basal cell carcinoma","volume":"29","author":"Lewis","year":"2007","journal-title":"Am. J. Dermatopathol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1097\/00001665-200603000-00027","article-title":"Squamous cell carcinoma of the auricle arising from keloid after radium needle therapy","volume":"17","author":"Mizuno","year":"2006","journal-title":"J. Craniofac. Surg."},{"key":"ref_43","unstructured":"Gutman, D., Codella, N.C.F., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (2016). Skin lesion analysis toward melanoma detection: A challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). arXiv."},{"key":"ref_44","unstructured":"Kaggle (2024, February 16). Keloid Scars vs Hypertrophic Scars. Available online: https:\/\/www.kaggle.com\/datasets\/quratulainislam\/keloid-vs-scar."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7999","DOI":"10.4081\/dr.2019.7999","article-title":"Seborrheic keratosis evolution into squamous cell carcinoma: A truly modified sun-related tumor? A case report and review of the literature","volume":"11","author":"Cimpean","year":"2019","journal-title":"Dermatol. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1038\/modpathol.2012.157","article-title":"Malignant dermatofibroma: Clinicopathological, immunohistochemical, and molecular analysis of seven cases","volume":"26","author":"Mentzel","year":"2013","journal-title":"Mod. Pathol."},{"key":"ref_47","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., 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_50","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_51","unstructured":"Tan, M., and Le, Q.V. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. arXiv.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_54","unstructured":"Tan, M., and Le, Q.V. (2021). EfficientNetV2: Smaller Models and Faster Training. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018). Learning Transferable Architectures for Scalable Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_56","first-page":"145","article-title":"Use of Xception Architecture for the Classification of Skin Lesions","volume":"12","author":"Tejada","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_57","first-page":"98","article-title":"Brain Tumor Classification Using InceptionResNetV2 and Transfer Learning","volume":"18","author":"Kurniawan","year":"2024","journal-title":"J. Comput. Biol. Med."},{"key":"ref_58","first-page":"384","article-title":"EfficientNetV2 Model for Breast Cancer Histopathological Image Classification","volume":"7","author":"Liu","year":"2022","journal-title":"IEEE Int. Conf. Electr. Eng. Comput. Sci."},{"key":"ref_59","first-page":"23","article-title":"Skin Cancer Classification Using NASNet","volume":"11","author":"Rahman","year":"2024","journal-title":"Int. J. Sci. Res. Arch."},{"key":"ref_60","first-page":"123","article-title":"Utilising VGG16 of Convolutional Neural Network for Brain Tumor and Alzheimer\u2019s Classification","volume":"10","author":"Ismail","year":"2024","journal-title":"J. Adv. Med. Sci."},{"key":"ref_61","first-page":"1123","article-title":"Classification of X-ray Images for Detection of Childhood Pneumonia using MobileNet","volume":"39","author":"Costa","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_62","first-page":"5182","article-title":"Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model","volume":"7","author":"Mulya","year":"2023","journal-title":"J. RESTI (Rekayasa Sist. Dan Teknol. Inf.)"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zerouaoui, H., and Idri, A. (2022). Classifying Breast Cytological Images using Deep Learning Architectures. Proc. Int. Conf. Data Sci. Artif. Intell., 557\u2013564.","DOI":"10.5220\/0010850000003123"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2228","DOI":"10.22201\/icat.24486736e.2024.22.1.2228","article-title":"Detection and classification of progressive supranuclear palsy from MRI images using deep learning","volume":"22","author":"Sait","year":"2024","journal-title":"J. Appl. Res. Technol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13005-016-0106-0","article-title":"Basal cell carcinoma, squamous cell carcinoma and melanoma of the head and face","volume":"12","author":"Feller","year":"2016","journal-title":"Head Face Med."},{"key":"ref_66","first-page":"136","article-title":"Skin diseases classification using deep learning methods","volume":"46","author":"Stanca","year":"2020","journal-title":"Curr. Health Sci. J."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/JBHI.2016.2635663","article-title":"An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification","volume":"21","author":"Kumar","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_70","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_71","unstructured":"Gupta, S., and Tan, M. (2025, January 07). EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks with AutoML. Available online: https:\/\/research.google\/blog\/efficientnet-edgetpu-creating-accelerator-optimized-neural-networks-with-automl\/."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.2147\/OTT.S113724","article-title":"Cutaneous basal cell carcinoma arising within a keloid scar: A case report","volume":"9","author":"Goder","year":"2016","journal-title":"OncoTargets Ther."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.mjafi.2018.07.018","article-title":"Squamous cell carcinoma arising in a keloid scar","volume":"75","author":"Majumder","year":"2019","journal-title":"Med. J. Armed Forces India"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1097\/PRS.0b013e3181b5a3ae","article-title":"Is radiation therapy for keloids acceptable? The risk of radiation-induced carcinogenesis","volume":"124","author":"Ogawa","year":"2009","journal-title":"Plast. Reconstr. Surg."},{"key":"ref_75","unstructured":"Kaggle (2024, February 08). Skin Cancer ISIC. Available online: https:\/\/www.kaggle.com\/datasets\/nodoubttome\/skin-cancer9-classesisic\/data."},{"key":"ref_76","unstructured":"International Skin Imaging Collaboration (ISIC) (2024, October 25). ISIC Gallery. Available online: https:\/\/gallery.isic-archive.com."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"180161","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Metzcar, J., Jutzeler, C.R., Macklin, P., K\u00f6hn-Luque, A., and Br\u00fcningk, S.C. (2024). A review of mechanistic learning in mathematical oncology. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1363144"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Brutti, F., La Rosa, F., Lazzeri, L., Benvenuti, C., Bagnoni, G., Massi, D., and Laurino, M. (2023). Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification. Bioengineering, 10.","DOI":"10.3390\/bioengineering10111322"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Bechelli, S., and Delhommelle, J. (2022). Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images. Bioengineering, 9.","DOI":"10.3390\/bioengineering9030097"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1111\/jdv.15935","article-title":"Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms","volume":"34","author":"Udrea","year":"2020","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"ref_82","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., and Marchetti, M. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC). arXiv."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (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"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/6\/710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:51:22Z","timestamp":1760028682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/6\/710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,12]]},"references-count":83,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["diagnostics15060710"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15060710","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,12]]}}}