Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Multimedia Tools and Applications Pub Date : 2024-09-10 DOI:10.1007/s11042-024-19974-3
Santosh Kumar Sharma, Debendra Muduli, Adyasha Rath, Sujata Dash, Ganapati Panda, Achyut Shankar, Dinesh Chandra Dobhal
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Abstract

Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and their variations, along with classifier like support vector machines (SVM), are frequently employed in such systems. This paper introduces a practical and enhanced system for detecting glaucoma in fundus images. The proposed model adresses the chanallages encountered by other existing models in recent litrature. Initially, we have employed contrast limited adaputive histogram equalization (CLAHE) to enhanced the visualization of input fundus inmages. Then, the discrete ripplet-II transform (DR2T) employing a degree of 2 for feature extraction. Afterwards, we have utilized a golden jackal optimization algorithm (GJO) employed to select the optimal features to reduce the dimension of the extracted feature vector. For classification purposes, we have employed a least square support vector machine (LS-SVM) equipped with three kernels: linear, polynomial, and radial basis function (RBF). This setup has been utilized to classify fundus images as either indicative of glaucoma or healthy. The proposed method is validated with the current state-of-the-art models on two standard datasets, namely, G1020 and ORIGA. The results obtained from our experimental result demonstrate that our best suggested approach DR2T+GJO+LS-SVM-RBF obtains better classification accuracy 93.38% and 97.31% for G1020 and ORIGA dataset with less number of features. It establishes a more streamlined network layout compared to conventional classifiers.

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离散涟漪-II 变换特征提取和元搜索优化特征选择用于使用最小平方支持向量机增强眼底图像中的青光眼检测功能
最近,利用眼底图像识别青光眼异常的计算机辅助诊断(CAD)系统的开发取得了重大进展。尽管小波及其变体等特征提取方法和支持向量机(SVM)等分类器存在缺陷,但仍经常被用于此类系统中。本文介绍了一种实用的增强型系统,用于检测眼底图像中的青光眼。所提出的模型解决了近年来其他现有模型所遇到的问题。首先,我们采用了对比度受限的自适应直方图均衡(CLAHE)来增强输入眼底图像的可视化。然后,我们使用离散涟漪-II 变换(DR2T)(阶数为 2)进行特征提取。然后,我们使用金豺优化算法(GJO)来选择最佳特征,以减少提取特征向量的维度。为了进行分类,我们采用了配备线性、多项式和径向基函数(RBF)三种内核的最小平方支持向量机(LS-SVM)。利用这种设置将眼底图像分类为青光眼或健康眼底图像。我们在两个标准数据集(即 G1020 和 ORIGA)上对所提出的方法与当前最先进的模型进行了验证。实验结果表明,我们建议的最佳方法 DR2T+GJO+LS-SVM-RBF 在 G1020 和 ORIGA 数据集上分别获得了 93.38% 和 97.31% 的较高分类准确率,且特征数量较少。与传统分类器相比,它建立了一个更精简的网络布局。
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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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