Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection  

作　　者：Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad 

机构地区：[1]Engineering Research Center of Intelligent Perception and Autonomous Control,Faculty of Information Technology,Beijing University of Technology,100124,Beijing,China [2]College of Information Science Technology,Hainan Normal University,Hainan Province,Haikou,571158,China [3]Department of Computer Science,University of Salford,Manchester,UK [4]Faculty of Information Technology Beijing University of Technology Chaoyang District,Beijing,China

出　　处：《Computers, Materials & Continua》2023年第7期1151-1172,共22页计算机、材料和连续体（英文）

基　　金：supported in part by the Beijing Natural Science Foundation(No.4212015);China Ministry of Education-China Mobile Scientific Research Foundation(No.MCM20200102).

摘　　要：Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized expertise.This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)method.The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global features.The enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for classification.To evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,respectively.The results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of Glaucoma.This study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关 键 词：Glaucoma detection grey golf optimization support vector machine feature extraction image classification 

分 类 号：R775[医药卫生—眼科]