Diabetic Retinopathy Lesion Discriminative Diagnostic System for Retinal Fundus Images

IF 0.8 Q4 ENGINEERING, BIOMEDICAL
C. Bhardwaj, Shruti Jain, M. Sood
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引用次数: 8

Abstract

Diabetic retinopathy (DR) is the main cause of retinal damage due to fluid leakage from blood vessels. Automated diagnostic systems assist the ophthalmologists practice manual lesion detection techniques which are tedious and time-consuming. A Diabetic Retinopathy Lesion Discrimination (DRLD) model is proposed for abnormality identification followed by DR lesion detection based on identification of DR pathological symptoms. Shape, intensity and gray-level co-occurrence matrix (GLCM) features are extracted from the identified lesions, and exhaustive statistical analysis is performed for optimal feature selection. Overall accura-cies of 97.9% and 91.5% are obtained using multi-layer perceptron neural network (MLPNN) and support vector machine (SVM) classifiers, respectively, for non-diseased versus diseased fundus image discrimination. MLPNN provides better performance for the fundus image discrimination approach, and further accuracy of 98.9% is obtained for DR lesion detection. When compared with other state-of-the-art techniques, the proposed approach provides better performance with significantly less computational complexity. A maximum accuracy improvement of 20.13% in fundus image discrimination and 5.90% in lesion categorization is achieved.
糖尿病视网膜病变视网膜眼底图像鉴别诊断系统
糖尿病性视网膜病变(DR)是引起视网膜损伤的主要原因,主要是由于血管中渗出液体。自动诊断系统帮助眼科医生实践手动病变检测技术,这是繁琐和耗时的。提出了一种糖尿病视网膜病变识别(DRLD)模型,用于异常识别,然后在识别DR病理症状的基础上进行DR病变检测。从已识别的病变中提取形状、强度和灰度共生矩阵(GLCM)特征,并进行详尽的统计分析以进行最优特征选择。使用多层感知器神经网络(MLPNN)和支持向量机(SVM)分类器对非病变和病变眼底图像进行判别,总体准确率分别为97.9%和91.5%。MLPNN为眼底图像识别方法提供了更好的性能,对DR病变的检测准确率达到98.9%。与其他最先进的技术相比,所提出的方法提供了更好的性能和显著降低的计算复杂度。眼底图像识别准确率提高20.13%,病灶分类准确率提高5.90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advanced Biomedical Engineering
Advanced Biomedical Engineering ENGINEERING, BIOMEDICAL-
CiteScore
1.40
自引率
10.00%
发文量
15
审稿时长
15 weeks
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