{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:01:57Z","timestamp":1774965717220,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Skin cancer, particularly melanoma, is one of the leading causes of cancer-related deaths. It is essential to detect and start the treatment in the early stages for it to be effective and to improve survival rates. This study developed and evaluated a deep learning-based classification model to classify the skin lesion images as benign (non-cancerous) and malignant (cancerous). In this study, we used the ISIC 2016 dataset to train the segmentation model and the Kaggle dataset of 10,000 images to train the classification model. We applied different data pre-processing techniques to enhance the robustness of our model. We used the segmentation model to generate a binary segmentation mask and used it with the corresponding pre-processed image by overlaying its edges to highlight the lesion region, before feeding it to the classification model. We used transfer learning, using ResNet-50 as a backbone model for a feedforward network. We achieved an accuracy of 92.80%, a precision of 98.64%, and a recall of 86.80%. From our study, we have found that integrating deep learning techniques with proper data pre-processing improves the model\u2019s performance. Future work will focus on expanding the datasets and testing more architectures to improve the performance metrics of the model.<\/jats:p>","DOI":"10.3390\/bdcc9040097","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T03:45:23Z","timestamp":1744343123000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning for Early Skin Cancer Detection: Combining Segmentation, Augmentation, and Transfer Learning"],"prefix":"10.3390","volume":"9","author":[{"given":"Ravi","family":"Karki","sequence":"first","affiliation":[{"name":"College of Engineering and Science, Victoria University, Sydney, NSW, 2000, Australia"}]},{"given":"Shishant","family":"G C","sequence":"additional","affiliation":[{"name":"College of Engineering and Science, Victoria University, Sydney, NSW, 2000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6851-0117","authenticated-orcid":false,"given":"Javad","family":"Rezazadeh","sequence":"additional","affiliation":[{"name":"College of Engineering and Science, Victoria University, Sydney, NSW, 2000, Australia"},{"name":"School of IT, Crown Institute of Higher Education (CIHE), Sydney, NSW 2060 Australia"}]},{"given":"Ammara","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Engineering and Science, Victoria University, Sydney, NSW, 2000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"ref_1","unstructured":"Leiter, U., Keim, U., and Garbe, C. (2024, May 19). Epidemiology of Skin Cancer: Update 2019. SpringerLink. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-46227-7_6."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aljanabi, M., \u00d6zok, Y.E., Rahebi, J., and Abdullah, A.S. (2018). Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm. Symmetry, 10.","DOI":"10.3390\/sym10080347"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gomathi, E., Jayasheela, M., Thamarai, M., and Geetha, M. (2023). Skin cancer detection using dual optimization based deep learning network. Biomed. Signal Process. Control, 84.","DOI":"10.1016\/j.bspc.2023.104968"},{"key":"ref_4","unstructured":"(2024, May 19). Web.archive.org. (n.d.). Wayback Machine. Available online: https:\/\/web.archive.org\/web\/20130318041656\/."},{"key":"ref_5","first-page":"67","article-title":"Skin Cancer Classification Using Transfer Learning by VGG16 Architecture (Case Study on Kaggle Dataset)","volume":"15","author":"Ibrahim","year":"2023","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Anand, V., Gupta, S., Altameem, A., Nayak, S.R., Poonia, R.C., and Saudagar, A.K.J. (2022). An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer. Diagnostics, 12.","DOI":"10.3390\/diagnostics12071628"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1007\/s11517-021-02473-0","article-title":"InSiNet: A deep convolutional approach to skin cancer detection and segmentation","volume":"60","author":"Reis","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Arabahmadi, M., Farahbakhsh, R., and Rezazadeh, J. (2022). Deep Learning for Smart Healthcare\u2014A Survey on Brain Tumor Detection from Medical Imaging. Sensors, 22.","DOI":"10.3390\/s22051960"},{"key":"ref_9","unstructured":"Gutman, D., Codella, N.C., 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_10","unstructured":"Javid, M.H. (2022). Melanoma Skin Cancer Dataset of 10000 Images [Data Set], Kaggle."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17833","DOI":"10.1007\/s11042-023-16273-1","article-title":"Deep learning in skin lesion analysis for malignant melanoma cancer identification","volume":"83","author":"Sivakumar","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","first-page":"33","article-title":"Skin cancer detection using neutrosophic c-means and fuzzy c-means clustering algorithms","volume":"8","author":"Abdelhafeez","year":"2023","journal-title":"J. Intell. Syst. Internet Things"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zafar, M., Amin, J., Sharif, M., Anjum, M.A., Mallah, G.A., and Kadry, S. (2023). DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification. Mathematics, 11.","DOI":"10.3390\/math11020364"},{"key":"ref_14","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Suk, H.I. (2017). An Introduction to Neural Networks and Deep Learning. Deep Learning for Medical Image Analysis, Academic Press.","DOI":"10.1016\/B978-0-12-810408-8.00002-X"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Lecture Notes in Computer Science"},{"key":"ref_17","unstructured":"Canny, J.F. (2024, August 19). Finding Edges and Lines in Images. Available online: http:\/\/hdl.handle.net\/1721.1\/6939."},{"key":"ref_18","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/97\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:12:34Z","timestamp":1760029954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/97"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,11]]},"references-count":19,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["bdcc9040097"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9040097","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,11]]}}}