Md. Kawsher Mahbub, Md. Zakir Hossain Zamil, Md. Abdul Mozid Miah, Partho Ghose, M. Biswas, K. Santosh
{"title":"mobapp4传染病:分类COVID-19,肺炎和结核病","authors":"Md. Kawsher Mahbub, Md. Zakir Hossain Zamil, Md. Abdul Mozid Miah, Partho Ghose, M. Biswas, K. Santosh","doi":"10.1109/CBMS55023.2022.00028","DOIUrl":null,"url":null,"abstract":"Illness due to infectious diseases has been always a global threat. Millions of people die per year due to COVID-19, pneumonia, and Tuberculosis (TB) as all of them infect the lungs. For all cases, early screening/diagnosis can help provide opportunities for better care. To handle this, we develop an application, which we call MobApp4InfectiousDisease that can identify abnormalities due to COVID-19, pneumonia, and TB using Chest X-ray image. In our MobApp4InfectiousDisease, we implemented a customized deep network with a single transfer learning technique. For validation, we offered in-depth experimental study and we achieved, for COVID-19-pneumonia-TB cases, accuracy of 97.72%196.62%199.75%, precision of 92.72%1100.0%199.29%, recall of 98.89%188.54%199.65%, and F1-score of 95.00%194.00%199.00%. Our results are compared with state-of-the-art techniques. To the best of our knowl-edge, this is the first time we deployed our proof-of-the-concept MobApp4InfectiousDisease for a multi-class infec-tious disease classification.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"MobApp4InfectiousDisease: Classify COVID-19, Pneumonia, and Tuberculosis\",\"authors\":\"Md. Kawsher Mahbub, Md. Zakir Hossain Zamil, Md. Abdul Mozid Miah, Partho Ghose, M. Biswas, K. Santosh\",\"doi\":\"10.1109/CBMS55023.2022.00028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Illness due to infectious diseases has been always a global threat. Millions of people die per year due to COVID-19, pneumonia, and Tuberculosis (TB) as all of them infect the lungs. For all cases, early screening/diagnosis can help provide opportunities for better care. To handle this, we develop an application, which we call MobApp4InfectiousDisease that can identify abnormalities due to COVID-19, pneumonia, and TB using Chest X-ray image. In our MobApp4InfectiousDisease, we implemented a customized deep network with a single transfer learning technique. For validation, we offered in-depth experimental study and we achieved, for COVID-19-pneumonia-TB cases, accuracy of 97.72%196.62%199.75%, precision of 92.72%1100.0%199.29%, recall of 98.89%188.54%199.65%, and F1-score of 95.00%194.00%199.00%. Our results are compared with state-of-the-art techniques. To the best of our knowl-edge, this is the first time we deployed our proof-of-the-concept MobApp4InfectiousDisease for a multi-class infec-tious disease classification.\",\"PeriodicalId\":218475,\"journal\":{\"name\":\"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS55023.2022.00028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS55023.2022.00028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MobApp4InfectiousDisease: Classify COVID-19, Pneumonia, and Tuberculosis
Illness due to infectious diseases has been always a global threat. Millions of people die per year due to COVID-19, pneumonia, and Tuberculosis (TB) as all of them infect the lungs. For all cases, early screening/diagnosis can help provide opportunities for better care. To handle this, we develop an application, which we call MobApp4InfectiousDisease that can identify abnormalities due to COVID-19, pneumonia, and TB using Chest X-ray image. In our MobApp4InfectiousDisease, we implemented a customized deep network with a single transfer learning technique. For validation, we offered in-depth experimental study and we achieved, for COVID-19-pneumonia-TB cases, accuracy of 97.72%196.62%199.75%, precision of 92.72%1100.0%199.29%, recall of 98.89%188.54%199.65%, and F1-score of 95.00%194.00%199.00%. Our results are compared with state-of-the-art techniques. To the best of our knowl-edge, this is the first time we deployed our proof-of-the-concept MobApp4InfectiousDisease for a multi-class infec-tious disease classification.