Detection of retinal abnormalities in fundus image using transfer learning networks
Detection of retinal abnormalities in fundus image using transfer learning networks
复制标题
使用迁移学习网络检测眼底图像中的视网膜异常
DOI：
10.1007/s00500-021-06088-3
复制
发表时间：
2021
期刊：
影响因子：
4.1
通讯作者：
A. Kamra
中科院分区：
文献类型：
--
作者：
M. Kaur;A. Kamra
糖尿病视网膜病变（DR）是与人眼相关的常见疾病之一，可导致失明。DR的检测非常重要，因为随着时间的推移，疾病会损害眼睛。目前，使用基于计算机辅助诊断的系统来帮助医疗从业者在早期阶段正确地检测DR。在这项工作中，视网膜图像的分类，这是由三个主要的模块。首先，使用CLAHE和DNCNN神经网络对图像进行预处理，这将减少图像中的诱导噪声。预处理后的图像，然后分割形态学和K均值算法。增强图像显示出更好的峰值信噪比。然后将分割的图像馈送到所提出的EyeNet，这是一种基于迁移学习的模型。EyeNet的架构基于ResNet-18。该网络在来自临床、DRIVE和STARE数据库的1500多张图像上进行了训练，准确率达到99.76%。
Diabetic retinopathy (DR) is one among the common disease associated with the human eye that can cause blindness. Detection of DR is very important as the disease will damage the eye with the passage of time. A computer-aided diagnosis-based system is used nowadays to assist the medical practitioner to correctly detect DR during the early stages. In this work, a retinal image’s classification is proposed, which is composed of three major blocks. Initially, the images are preprocessed using CLAHE and DNCNN neural networks, which will reduce the induced noise in the images. Preprocessed images then segmented using morphological and K-mean algorithms. The enhanced images have shown a better peak signal-to-noise ratio. The segmented images are then fed to the proposed EyeNet, which is a transfer learning-based model. The architecture of EyeNet is based on ResNet-18. The network is trained on more than 1500 images from clinical, DRIVE, and STARE databases and has shown an accuracy of 99.76%.
影响因子：
2.7
作者：
Rahman, Tawsifur;Chowdhury, Muhammad E. H.;Kashem, Saad
通讯作者：
Kashem, Saad