{"title":"将注意机制整合到ResNet模型中的肺结节图像分类","authors":"Khai Dinh Lai, T. Le, T. T. Nguyen","doi":"10.1109/KSE56063.2022.9953758","DOIUrl":null,"url":null,"abstract":"In this research, in order to accurately diagnose lung nodules using the LUNA16 dataset, a deep learning model, ResNetl01, is analyzed and chosen. The paper includes: (1) demonstrating the efficiency of the ResNetl01 network on the LUNA16; (2) analyzing the benefits and drawbacks of Attention modules before selecting the best Attention module to integrate into the ResNetl01 model in the classification of lung nodules in CT scans challenge; (3) comparing the efficacy of the proposed model to prior outcomes to demonstrate the model’s feasibility.","PeriodicalId":330865,"journal":{"name":"2022 14th International Conference on Knowledge and Systems Engineering (KSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image classification of lung nodules by requiring the integration of Attention Mechanism into ResNet model\",\"authors\":\"Khai Dinh Lai, T. Le, T. T. Nguyen\",\"doi\":\"10.1109/KSE56063.2022.9953758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this research, in order to accurately diagnose lung nodules using the LUNA16 dataset, a deep learning model, ResNetl01, is analyzed and chosen. The paper includes: (1) demonstrating the efficiency of the ResNetl01 network on the LUNA16; (2) analyzing the benefits and drawbacks of Attention modules before selecting the best Attention module to integrate into the ResNetl01 model in the classification of lung nodules in CT scans challenge; (3) comparing the efficacy of the proposed model to prior outcomes to demonstrate the model’s feasibility.\",\"PeriodicalId\":330865,\"journal\":{\"name\":\"2022 14th International Conference on Knowledge and Systems Engineering (KSE)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 14th International Conference on Knowledge and Systems Engineering (KSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KSE56063.2022.9953758\",\"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 14th International Conference on Knowledge and Systems Engineering (KSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KSE56063.2022.9953758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image classification of lung nodules by requiring the integration of Attention Mechanism into ResNet model
In this research, in order to accurately diagnose lung nodules using the LUNA16 dataset, a deep learning model, ResNetl01, is analyzed and chosen. The paper includes: (1) demonstrating the efficiency of the ResNetl01 network on the LUNA16; (2) analyzing the benefits and drawbacks of Attention modules before selecting the best Attention module to integrate into the ResNetl01 model in the classification of lung nodules in CT scans challenge; (3) comparing the efficacy of the proposed model to prior outcomes to demonstrate the model’s feasibility.