{"title":"基于集成深度学习分类器的黑色素瘤癌最优检测","authors":"M. Maheswari, A. Aloysius, P. Purusothaman","doi":"10.1109/ICEEICT56924.2023.10157099","DOIUrl":null,"url":null,"abstract":"The widespread adoption of electronic health record (EHR) systems in response to a diverse array of requirements for primary and secondary healthcare, there is now an abundance of clinical data that can be accessed with relative ease. This is a significant improvement over the situation that existed previously. The widespread implementation of EHR systems is directly responsible for this effect. Unstructured clinical reports that were either transcribed or dictated by clinicians make up a sizeable percentage of these data, and they were collected in that format. In this paper, we develop an ensemble model to classify cancer disease from EHR using several convolutional neural network (CNN). The simulation is conducted to test the efficacy of the model and the results show that the proposed method achieves higher classification rate than other methods.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ensemble Deep Learning Classifier for Optimal Detection of Melanoma Cancer\",\"authors\":\"M. Maheswari, A. Aloysius, P. Purusothaman\",\"doi\":\"10.1109/ICEEICT56924.2023.10157099\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The widespread adoption of electronic health record (EHR) systems in response to a diverse array of requirements for primary and secondary healthcare, there is now an abundance of clinical data that can be accessed with relative ease. This is a significant improvement over the situation that existed previously. The widespread implementation of EHR systems is directly responsible for this effect. Unstructured clinical reports that were either transcribed or dictated by clinicians make up a sizeable percentage of these data, and they were collected in that format. In this paper, we develop an ensemble model to classify cancer disease from EHR using several convolutional neural network (CNN). The simulation is conducted to test the efficacy of the model and the results show that the proposed method achieves higher classification rate than other methods.\",\"PeriodicalId\":345324,\"journal\":{\"name\":\"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEICT56924.2023.10157099\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEICT56924.2023.10157099","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ensemble Deep Learning Classifier for Optimal Detection of Melanoma Cancer
The widespread adoption of electronic health record (EHR) systems in response to a diverse array of requirements for primary and secondary healthcare, there is now an abundance of clinical data that can be accessed with relative ease. This is a significant improvement over the situation that existed previously. The widespread implementation of EHR systems is directly responsible for this effect. Unstructured clinical reports that were either transcribed or dictated by clinicians make up a sizeable percentage of these data, and they were collected in that format. In this paper, we develop an ensemble model to classify cancer disease from EHR using several convolutional neural network (CNN). The simulation is conducted to test the efficacy of the model and the results show that the proposed method achieves higher classification rate than other methods.