{"title":"A New Deep Learning Model for Skin Cancer Classification","authors":"Melisa Uçkuner, H. Erol","doi":"10.1109/UBMK52708.2021.9558936","DOIUrl":null,"url":null,"abstract":"Cancer is a group of diseases that damage tissues by the uncontrolled proliferation of cells. The difficulty of distinguishing skin cancer, which is a common type of cancer, without technical support necessitates studies that can help specialists in the diagnosis phase. In this study, a deep learning model with 7 convolution layers and 3 neural layers was designed to classify the HAM10000 dataset, which consists of 7 classes and includes dermoscopic images. The accuracy rate for the test data of the proposed model was calculated as 99.01%. This result shows that the proposed model can help experts in diagnosing skin cancer.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th International Conference on Computer Science and Engineering (UBMK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UBMK52708.2021.9558936","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Cancer is a group of diseases that damage tissues by the uncontrolled proliferation of cells. The difficulty of distinguishing skin cancer, which is a common type of cancer, without technical support necessitates studies that can help specialists in the diagnosis phase. In this study, a deep learning model with 7 convolution layers and 3 neural layers was designed to classify the HAM10000 dataset, which consists of 7 classes and includes dermoscopic images. The accuracy rate for the test data of the proposed model was calculated as 99.01%. This result shows that the proposed model can help experts in diagnosing skin cancer.