{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/comparative-study-on-different-deep-learning","title":"Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approach","arxiv_id":null,"date":"2021-01-03","proceeding":"International Journal of Scientific and Research Publication 2021 1","authors":["Saswat Panda","Abhishek Sunil Tiwari","Manas Ranjan Prusty"],"abstract":"Developing countries, specifically India, do not have sufficient hospitals and doctors to reach out to the population.\r\nForget about skin specialists, there are still thousands of villages without even a basic hospital. But, there is one thing that\r\nreaches out to every person in this country which is the internet. This research paper focuses on training different pre-trained\r\nmodels on our dataset and suggests the best one for skin lesion classification. This model has been used with a mobile compatible web app to detect skin cancer at the earliest stage possible. Hence, the objective of this research paper is to develop\r\nan intelligent system to detect skin cancer at the earliest stage possible by a skin lesion classifier. The dataset includes different\r\ncategories of diseases like Actinic Keratosis, Vascular Lesions, Lentigo, Melanoma, and Dermatofibroma. The pre-trained\r\nmodels used in this research are the different transfer learning methods like VGG19, Xception, Densenet, Inception, MobileNet,\r\nNasNetMobile, and Resnet. Using transfer learning, Deep Neural Networks can be trained with presumably less amount of\r\ndata. Also, transfer learning has been consistently proven to reduce training time as well as boost model accuracy. Hence, this\r\npaper has given the emphasis on using transfer learning models instead of building a model from scratch.","url_abs":"http://dx.doi.org/10.29322/IJSRP.11.01.2021.p10923","url_pdf":"https://www.ijsrp.org/research-paper-0121/ijsrp-p10923.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"comparative-study-on-different-deep-learning","repo_url":"https://github.com/Abhishek-st/Skin-Lesion-Analysis","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"lesion-classification","task_name":"Lesion Classification"},{"task_slug":"skin-lesion-classification","task_name":"Skin Lesion Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}