糖尿病视网膜病变影像中视网膜病变等级和黄斑水肿风险分类的有效框架

B. Balasuganya, A. Chinnasamy, D. Sheela
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引用次数: 1

摘要

众所周知,对于糖尿病患者来说,糖尿病视网膜病变(DR)是一种迅速扩散的感染,最终导致视力完全丧失。因此,对于糖尿病患者来说,事先识别DR是保护眼睛的重要问题,并为及时治疗提供支持。DR识别应该在物理上是可能的,因此也可以区分。在以前的框架中,评估视网膜眼底图像以检查微动脉瘤(MA)、渗出物、出血、黄斑和静脉的语音变化是一项漫长而昂贵的工作。然而,在机器人化框架中,可以利用图像处理策略进行预识别。本文提出了一种DR发现框架。首先,利用交叉CLAHE和圆形平均滤波器对信息图像进行预处理,通过Coye滤波器提取法向通道和纹理;短时间后,图像显示不规则分裂,其中MA分裂,出血,渗出和新生血管被传递。几乎36个不同的亮点从分段图片中删除。采用半种salp - swarm-猫科动物群体推进(CSO)算法选择合适的亮点。最后,利用改进后的RNN-LSTM进行了排序。传达三个命令,(I)视网膜病变的种类分类,(ii)视网膜病变的评估分类,(iii)黄斑水肿(ME)的风险。顺序的正确性是:一般DR 99.73%, NPDR等级95.6% NPDR黄斑水肿风险99.4%,PDR黄斑水肿风险92.3%。仿真结果表明,采用决策树(DT)算法和随机森林(RF)算法,该框架在影响精度、显式精度和精度方面都有较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Effective Framework for the Classification of Retinopathy Grade and Risk of Macular Edema for Diabetic Retinopathy Images
It is well know that for a diabetic patient, Diabetic Retinopathy (DR) is a speedy spreading infection which results in total loss of vision. Hence for diabetic patient, prior DR identification is important issue to protect eyes furthermore supportive for opportune treatment. The DR identification should be possible physically and could likewise distinguished consequently. In previous framework, assessment of fundus pictures of retina for checking the phonological variety in Micro Aneurysms (MA), exudates, hemorrhages, macula and veins is a drawn-out and lavish errand. However in the robotized framework, picture handling strategies can be utilized for before DR identification. Here, a framework for DR discovery is proposed. To start with, the information picture is pre-prepared utilizing crossover CLAHE and circular average filter round normal channel and veins are extricated by Coye Filter. A short time later, picture is exposed to irregularities division, where division of MA, hemorrhages, exudates, and neovascularization are conveyed. Almost 36 distinct highlights are removed from sectioned pictures. A half breed salp swarm-feline multitude advancement (CSO) calculation is used for choosing the appropriate highlights. At last, an arrangement is conveyed by changed RNN-LSTM. Three orders are conveyed, (I) Classification of kind of retinopathy, (ii) Classification of evaluation of retinopathy, (iii) Risk of Macular Edema (ME). The order correctness’s got are: 99.73% for kind of DR, 95.6% for NPDR grade and 99.4% for NPDR Macular Edema Risk, 92.3% for PDR Macular Edema Risk. Our simulation results reveals that with Decision Tree (DT) and Random Forest (RF) Algorithm, this framework provides better results in terms of accuracy of affectability and explicitness and Precision.
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