{"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/automated-skin-lesion-classification-using","title":"Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge","arxiv_id":"1901.10802","date":"2019-01-30","proceeding":null,"authors":["Md Ashraful Alam Milton"],"abstract":"In this paper, we studied extensively on different deep learning based\nmethods to detect melanoma and skin lesion cancers. Melanoma, a form of\nmalignant skin cancer is very threatening to health. Proper diagnosis of\nmelanoma at an earlier stage is crucial for the success rate of complete cure.\nDermoscopic images with Benign and malignant forms of skin cancer can be\nanalyzed by computer vision system to streamline the process of skin cancer\ndetection. In this study, we experimented with various neural networks which\nemploy recent deep learning based models like PNASNet-5-Large,\nInceptionResNetV2, SENet154, InceptionV4. Dermoscopic images are properly\nprocessed and augmented before feeding them into the network. We tested our\nmethods on International Skin Imaging Collaboration (ISIC) 2018 challenge\ndataset. Our system has achieved best validation score of 0.76 for\nPNASNet-5-Large model. Further improvement and optimization of the proposed\nmethods with a bigger training dataset and carefully chosen hyper-parameter\ncould improve the performances. The code available for download at\nhttps://github.com/miltonbd/ISIC_2018_classification","url_abs":"http://arxiv.org/abs/1901.10802v1","url_pdf":"http://arxiv.org/pdf/1901.10802v1.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":"automated-skin-lesion-classification-using","repo_url":"https://github.com/miltonbd/ISIC_2018_classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"automated-skin-lesion-classification-using","repo_url":"https://github.com/MS-Mind/MS-Code-06/tree/main/pnasnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"automated-skin-lesion-classification-using","repo_url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/pnasnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lesion-classification","task_name":"Lesion Classification"},{"task_slug":"skin-lesion-classification","task_name":"Skin Lesion Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.10802","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10802"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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