Pham Van-Nguyen, Le Duc-Tai, Bum Junghyun, Kim Seong Ho, Song Su Jeong, Choo Hyunseung
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Bioengineering (Basel). 2023 Sep 6;10(9):1048. doi: 10.3390/bioengineering10091048.
Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes.
超广角眼底图像（UFI）因其能够捕捉视网膜的广阔视野，已成为眼科医生诊断眼部疾病的关键工具。然而，在这种成像方式中检测和分类多种疾病对眼科医生来说仍然是一项重大挑战。用于UFI的自动疾病分类系统可以帮助眼科医生做出更快、更准确的诊断。然而，现有的UFI分类方法通常只关注单一疾病，或者在处理多疾病问题时假设每个图像只包含一种疾病。此外，每种疾病的独特特征通常未被用于提高分类系统的性能。为了解决这些局限性，我们提出了一种新颖的方法，该方法利用特定疾病的感兴趣区域进行UFI的多标签分类。我们的方法使用三个区域，包括视盘区域、黄斑区域和整个UFI，这些区域是诊断一种或多种眼部疾病的最具信息性的区域。在一个包含5930张患有六种常见眼部疾病的UFI的数据集上的实验结果表明，我们提出的方法取得了优异的性能，每个类别的受试者操作特征曲线下面积得分从95.07%到99.14%不等。这些结果不仅超过了现有的最先进方法，而且还显示出显著的提升，提升幅度高达5.29%。这些结果证明了我们的方法有潜力为眼科医生提供有价值的信息，用于眼部疾病的早期准确诊断，最终改善患者的治疗效果。
