Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2692-2695. doi: 10.1109/EMBC46164.2021.9630075.
Diabetic Retinopathy is a major cause of vision loss caused by retina lesions, including hard and soft exudates, microaneurysms, and hemorrhages. The development of a computational tool capable of detecting these lesions can assist in the early diagnosis of the most severe forms of the lesions and assist in the screening process and definition of the best treatment form. This paper proposes a computational model based on pre-trained convolutional neural networks capable of detecting fundus lesions to promote medical diagnosis support. The model was trained, adjusted, and evaluated using the DDR Diabetic Retinopathy dataset and implemented based on a YOLOv4 architecture and Darknet framework, reaching an mAP of 11.13% and a mIoU of 13.98%. The experimental results show that the proposed model presented results superior to those obtained in related works found in the literature.
糖尿病视网膜病变是由视网膜病变引起的主要视力丧失原因，包括硬性和软性渗出物、微动脉瘤和出血。开发一种能够检测这些病变的计算工具可以帮助早期诊断病变的最严重形式，并有助于筛查过程和确定最佳治疗形式。本文提出了一种基于预训练卷积神经网络的计算模型，能够检测眼底病变，以促进医学诊断支持。该模型使用 DDR 糖尿病视网膜病变数据集进行训练、调整和评估，并基于 YOLOv4 架构和 Darknet 框架实现，达到了 11.13%的 mAP 和 13.98%的 mIoU。实验结果表明，所提出的模型的结果优于文献中发现的相关工作的结果。
