{"title":"基于ResNet + CBAM注意机制的疟疾检测","authors":"Nan Yang, Chunlin He","doi":"10.1109/ISPDS56360.2022.9874134","DOIUrl":null,"url":null,"abstract":"Aiming at the low accuracy and time-consuming training of malaria detection, this paper proposes a malaria detection algorithm based on ResNet+CBAM attention mechanism. In the ResNet-40 model, which reduces the number of network layers and network width, the CBAM attention mechanism module is added and trained on the malaria dataset (Malaria dataset). The experimental results show that the detection method proposed in this paper improves the classification accuracy by 1% on the original basis.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"212 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Malaria detection based on ResNet + CBAM attention mechanism\",\"authors\":\"Nan Yang, Chunlin He\",\"doi\":\"10.1109/ISPDS56360.2022.9874134\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the low accuracy and time-consuming training of malaria detection, this paper proposes a malaria detection algorithm based on ResNet+CBAM attention mechanism. In the ResNet-40 model, which reduces the number of network layers and network width, the CBAM attention mechanism module is added and trained on the malaria dataset (Malaria dataset). The experimental results show that the detection method proposed in this paper improves the classification accuracy by 1% on the original basis.\",\"PeriodicalId\":280244,\"journal\":{\"name\":\"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)\",\"volume\":\"212 5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPDS56360.2022.9874134\",\"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 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPDS56360.2022.9874134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Malaria detection based on ResNet + CBAM attention mechanism
Aiming at the low accuracy and time-consuming training of malaria detection, this paper proposes a malaria detection algorithm based on ResNet+CBAM attention mechanism. In the ResNet-40 model, which reduces the number of network layers and network width, the CBAM attention mechanism module is added and trained on the malaria dataset (Malaria dataset). The experimental results show that the detection method proposed in this paper improves the classification accuracy by 1% on the original basis.
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