{"title":"是否使用裁剪技术:对基于svm的OCT图像AMD检测的影响","authors":"C. Ko, Po-Han Chen, Wei-Ming Liao, Cheng-Kai Lu, Cheng-Hung Lin, Jing-Wen Liang","doi":"10.1109/AICAS.2019.8771609","DOIUrl":null,"url":null,"abstract":"This paper compares the system performance of distinct flows with automatic image cropping to without automatic image cropping for age-related macular degeneration (AMD) detection on optical coherence tomography (OCT) images. Using the image cropping, the computational time of noise removal and feature extraction can be significantly reduced by a small loss of detection accuracy. The simulation results show that using the image cropping at the first stage achieves 93.4% accuracy. Compared to the flow without image cropping, using the image cropping loses only 0.5% accuracy but saves about 12 hours computational time and about a half of memory storages.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"59 21","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Using A Cropping Technique or Not: Impacts on SVM-based AMD Detection on OCT Images\",\"authors\":\"C. Ko, Po-Han Chen, Wei-Ming Liao, Cheng-Kai Lu, Cheng-Hung Lin, Jing-Wen Liang\",\"doi\":\"10.1109/AICAS.2019.8771609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper compares the system performance of distinct flows with automatic image cropping to without automatic image cropping for age-related macular degeneration (AMD) detection on optical coherence tomography (OCT) images. Using the image cropping, the computational time of noise removal and feature extraction can be significantly reduced by a small loss of detection accuracy. The simulation results show that using the image cropping at the first stage achieves 93.4% accuracy. Compared to the flow without image cropping, using the image cropping loses only 0.5% accuracy but saves about 12 hours computational time and about a half of memory storages.\",\"PeriodicalId\":273095,\"journal\":{\"name\":\"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"volume\":\"59 21\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICAS.2019.8771609\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICAS.2019.8771609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using A Cropping Technique or Not: Impacts on SVM-based AMD Detection on OCT Images
This paper compares the system performance of distinct flows with automatic image cropping to without automatic image cropping for age-related macular degeneration (AMD) detection on optical coherence tomography (OCT) images. Using the image cropping, the computational time of noise removal and feature extraction can be significantly reduced by a small loss of detection accuracy. The simulation results show that using the image cropping at the first stage achieves 93.4% accuracy. Compared to the flow without image cropping, using the image cropping loses only 0.5% accuracy but saves about 12 hours computational time and about a half of memory storages.