{"title":"基于深度学习方法的乳腺癌图像分类","authors":"Clenitta Joseph M, Bipin P R, Bobby Mathews C","doi":"10.1109/ACCESS57397.2023.10200365","DOIUrl":null,"url":null,"abstract":"Breast cancer is a type of cancer that begins in the breast cell. The only options to lessen the damage are early discovery and appropriate treatment. People willfully disregard physical problems in their bodies due to ignorance or a lack of detection technology. Deep learning is being used more frequently in the field of medical science, and it is good at a variety of tasks, including segmentation, detection, and classification. This article focus on the breast cancer images classification using VGG-16 and VGG-19. Analyze their models, precision, and a number of other aspects. The accuracy of image classification in VGG-16 and VGG-19 is 91% and 92%, respectively.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Breast Cancer Image Classification Using Deep Learning Methods\",\"authors\":\"Clenitta Joseph M, Bipin P R, Bobby Mathews C\",\"doi\":\"10.1109/ACCESS57397.2023.10200365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Breast cancer is a type of cancer that begins in the breast cell. The only options to lessen the damage are early discovery and appropriate treatment. People willfully disregard physical problems in their bodies due to ignorance or a lack of detection technology. Deep learning is being used more frequently in the field of medical science, and it is good at a variety of tasks, including segmentation, detection, and classification. This article focus on the breast cancer images classification using VGG-16 and VGG-19. Analyze their models, precision, and a number of other aspects. The accuracy of image classification in VGG-16 and VGG-19 is 91% and 92%, respectively.\",\"PeriodicalId\":345351,\"journal\":{\"name\":\"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACCESS57397.2023.10200365\",\"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 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACCESS57397.2023.10200365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Breast Cancer Image Classification Using Deep Learning Methods
Breast cancer is a type of cancer that begins in the breast cell. The only options to lessen the damage are early discovery and appropriate treatment. People willfully disregard physical problems in their bodies due to ignorance or a lack of detection technology. Deep learning is being used more frequently in the field of medical science, and it is good at a variety of tasks, including segmentation, detection, and classification. This article focus on the breast cancer images classification using VGG-16 and VGG-19. Analyze their models, precision, and a number of other aspects. The accuracy of image classification in VGG-16 and VGG-19 is 91% and 92%, respectively.