M. Sadiq, Donthi Sankalpa, Karam Ahfid, A. Sagahyroon, S. Dhou
{"title":"基于支持向量机分类的黑色素瘤初步检测移动应用","authors":"M. Sadiq, Donthi Sankalpa, Karam Ahfid, A. Sagahyroon, S. Dhou","doi":"10.1109/iCCECE49321.2020.9231259","DOIUrl":null,"url":null,"abstract":"This paper proposes a mobile application that uses a mobile phone camera attached to an enhanced lens to capture images of any suspicious portrusions on the body (e.g. mole) and be able to predict whether it is melanoma using image processing and machine learning techniques. The images are preprocessed to remove the noise and segment the region of interest (ROI). Features that distinguish melanoma from normal tissues are extracted such as the texture, color, and geometrical shape. The proposed method uses Support Vector Machine (SVM) classification algorithm for training and prediction. The proposed method is implemented and tested on publicly available datasets. Experimantal results showed that the method was able to detect the melanoma cases with a prediction accuracy of 79%.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Preliminary Melanoma Detection Mobile Application using Support Vector Machine Classification\",\"authors\":\"M. Sadiq, Donthi Sankalpa, Karam Ahfid, A. Sagahyroon, S. Dhou\",\"doi\":\"10.1109/iCCECE49321.2020.9231259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a mobile application that uses a mobile phone camera attached to an enhanced lens to capture images of any suspicious portrusions on the body (e.g. mole) and be able to predict whether it is melanoma using image processing and machine learning techniques. The images are preprocessed to remove the noise and segment the region of interest (ROI). Features that distinguish melanoma from normal tissues are extracted such as the texture, color, and geometrical shape. The proposed method uses Support Vector Machine (SVM) classification algorithm for training and prediction. The proposed method is implemented and tested on publicly available datasets. Experimantal results showed that the method was able to detect the melanoma cases with a prediction accuracy of 79%.\",\"PeriodicalId\":413847,\"journal\":{\"name\":\"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iCCECE49321.2020.9231259\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iCCECE49321.2020.9231259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Preliminary Melanoma Detection Mobile Application using Support Vector Machine Classification
This paper proposes a mobile application that uses a mobile phone camera attached to an enhanced lens to capture images of any suspicious portrusions on the body (e.g. mole) and be able to predict whether it is melanoma using image processing and machine learning techniques. The images are preprocessed to remove the noise and segment the region of interest (ROI). Features that distinguish melanoma from normal tissues are extracted such as the texture, color, and geometrical shape. The proposed method uses Support Vector Machine (SVM) classification algorithm for training and prediction. The proposed method is implemented and tested on publicly available datasets. Experimantal results showed that the method was able to detect the melanoma cases with a prediction accuracy of 79%.