基于深度学习的糖尿病眼底病变检测研究  

Detection of Diabetic Fundus Disease Based on Deep Learning

作　　者：侯高峰 房丰洲[1,2] Hou Gaofeng;Fang Fengzhou(State Key Laboratory of Precision Measuring Technology and Instruments,Tianjin University,Tianjin 300072,China;Laboratory of Micro/Nano Manufacturing Technology,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学精密测试技术及仪器国家重点实验室,天津300072 [2]天津大学微纳制造实验室,天津300072

出　　处：《激光与光电子学进展》2023年第2期274-280,共7页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(52035009)。

摘　　要：糖尿病不仅会增加视网膜血管疾病的风险,严重时甚至会发展成为糖尿病视网膜病变。糖尿病视网膜病变的4种典型病理特征是微动脉瘤、出血、硬性渗出物和软性渗出物。随着机器学习尤其是深度学习的发展,智能辅助诊断医疗已经成为一种趋势,智能辅助诊断的前提是可以定性定量地提取出相应的病变区域。提出了一种基于深度学习级联架构参数优化的眼底病变检测模型,该模型有效解决了眼底病变的多尺度和小目标问题,在DDR数据集上检测病变的综合测试精度达0.380,检测性能优于目前主流的检测网络。Diabetes would increase the risk of retinal vascular disease,and may further develop into diabetic retinopathy in severe cases.Among all pathological features of diabetic retinopathy,microaneurysms,bleeding,hard exudates,and soft exudates are usually typical.In recent years,with the development of deep learning,intelligent assisted diagnostic medicine has become a trend.The premise of intelligent aided diagnosis is that the corresponding lesion area can be extracted qualitatively and quantitatively.Therefore,a model of fundus lesion detection is proposed in this paper with cascade architecture parameter optimization,which effectively solves the multi-scale and small target problems of fundus lesions.The comprehensive test accuracy of detecting lesions on DDR dataset can reach 0.380,which is better in detection performance than the current mainstream detection network.

关 键 词：医用光学 图像处理 深度学习 糖尿病视网膜病变 小目标检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]