Two-step hierarchical neural network for classification of dry age-related macular degeneration using optical coherence tomography images
文献类型：期刊论文
| 作者 | Hu, Min6; Wu, Bin2; Lu, Di3; Xie, Jing6; Chen, Yiqiang4; Yang, Zhikuan5; Dai, Weiwei1,6 |
| 刊名 | FRONTIERS IN MEDICINE
 |
| 出版日期 | 2023-07-19 |
| 卷号 | 10页码:12 |
| 关键词 | optical coherence tomography (OCT) age-related macular degeneration (AMD) nascent geographic atrophy (nGA) convolutional neural network (CNN) deep learning |
| DOI | 10.3389/fmed.2023.1221453 |
| 英文摘要 | PurposeThe aim of this study is to apply deep learning techniques for the development and validation of a system that categorizes various phases of dry age-related macular degeneration (AMD), including nascent geographic atrophy (nGA), through the analysis of optical coherence tomography (OCT) images. MethodsA total of 3,401 OCT macular images obtained from 338 patients admitted to Shenyang Aier Eye Hospital in 2019-2021 were collected for the development of the classification model. We adopted a convolutional neural network (CNN) model and introduced hierarchical structure along with image enhancement techniques to train a two-step CNN model to detect and classify normal and three phases of dry AMD: atrophy-associated drusen regression, nGA, and geographic atrophy (GA). Five-fold cross-validation was used to evaluate the performance of the multi-label classification model. ResultsExperimental results obtained from five-fold cross-validation with different dry AMD classification models show that the proposed two-step hierarchical model with image enhancement achieves the best classification performance, with a f1-score of 91.32% and a kappa coefficients of 96.09% compared to the state-of-the-art models. The results obtained from the ablation study demonstrate that the proposed method not only improves accuracy across all categories in comparison to a traditional flat CNN model, but also substantially enhances the classification performance of nGA, with an improvement from 66.79 to 81.65%. ConclusionThis study introduces a novel two-step hierarchical deep learning approach in categorizing dry AMD progression phases, and demonstrates its efficacy. The high classification performance suggests its potential for guiding individualized treatment plans for patients with macular degeneration. |
| 资助项目 | Science and Innovation Foundation of Hunan Province of China[2020SK50110] ; Science and Innovation Leadership Plan of Hunan Province of China[2021GK4015] |
| WOS研究方向 | General & Internal Medicine |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| WOS记录号 | WOS:001040512400001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21285]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Dai, Weiwei |
| 作者单位 | 1.Anhui Med Univ, Anhui Aier Eye Hosp, Hefei, Peoples R China 2.Shenyang Aier Excellence Eye Hosp, Dept Retina, Shenyang, Peoples R China 3.Shenyang Aier Optometry Hosp, Dept Retina, Shenyang, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 5.Aier Inst Optometry & Vis Sci, Changsha, Peoples R China 6.Changsha Aier Eye Hosp, Changsha, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Min,Wu, Bin,Lu, Di,et al. Two-step hierarchical neural network for classification of dry age-related macular degeneration using optical coherence tomography images[J]. FRONTIERS IN MEDICINE,2023,10:12. |
| APA | Hu, Min.,Wu, Bin.,Lu, Di.,Xie, Jing.,Chen, Yiqiang.,...&Dai, Weiwei.(2023).Two-step hierarchical neural network for classification of dry age-related macular degeneration using optical coherence tomography images.FRONTIERS IN MEDICINE,10,12. |
| MLA | Hu, Min,et al."Two-step hierarchical neural network for classification of dry age-related macular degeneration using optical coherence tomography images".FRONTIERS IN MEDICINE 10(2023):12. |
入库方式： OAI收割
来源：计算技术研究所
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