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オゴハラ カズノリ
OGOHARA Kazunori
小郷原 一智 所属 京都産業大学 理学部 宇宙物理・気象学科 職種 准教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2018/05/30 |
| 形態種別 | 研究論文（国際会議プロシーディングス） |
| 査読 | 査読あり |
| 標題 | Automatic microaneurysms detection on retinal images using deep convolution neural network |
| 執筆形態 | その他 |
| 掲載誌名 | 2018 International Workshop on Advanced Image Technology, IWAIT 2018 |
| 出版社・発行元 | Institute of Electrical and Electronics Engineers Inc. |
| 巻・号・頁 | pp.1-2 |
| 著者・共著者 | Yuji Hatanaka,Kazunori Ogohara,Wataru Sunayama,Mitsuhiro Miyashita,Chisako Muramatsu,Hiroshi Fujita |
| 概要 | Visual loss can be prevented by early detection and treatment of disease. Diabetic retinopathy is the leading cause of vision loss, and microaneurysms (MAs) are an early symptom of this disease. The fundus examination is effective at early detection of diabetic retinopathy. However, detecting MAs on retinal images is difficult for physicians because MAs typically appear as small dark dots. Therefore, many studies on automated MA detection have been conducted. This study itself proposes an MA detector that combines three existing types of detectors: the double-ring filter, shape index based on the Hessian matrix, and Gabor filter. However, because deep convolutional neural networks (DCNN) have shown superior performance in image recognition studies, this study conducts automated MA detection using DCNN. The proposed method is structured with a two-step DCNN and three-layer perceptron with 48 features for false positives (FPs) reduction. In the two-step DCNN, the first DCNN is for initial MA detection and the second DCNN is for FPs reduction. By applying the proposed method to the DIARETDB1 database, the proposed method shows superior performance. |
| DOI | 10.1109/IWAIT.2018.8369794 |
| Put Code(ORCID) | 52343311 |
| PermalinkURL | http://www.scopus.com/inward/record.url?eid=2-s2.0-85048814520&partnerID=MN8TOARS |
| researchmap用URL | http://orcid.org/0000-0001-7666-4442 |