A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis  

作　　者：Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim 

机构地区：[1]School of Software,Northwestern Polytechnical University,Xi’an,710072,China [2]Shifa College of Medicine,Shifa Tameer-e-millat University,Islamabad,44000,Pakistan [3]College of Information Science and Technology,Hainan Normal University,Haikou,571158,China [4]Department of Computer Science and Engineering,College of Applied Studies and Community Service,King Saud University,Riyadh,11495,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期539-567,共29页工程与科学中的计算机建模(英文)

基　　金：Researchers Supporting Project number(RSP2025R314),King Saud University,Riyadh,Saudi Arabia.

摘　　要：Glaucoma,a leading cause of blindness,demands early detection for effective management.While AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size variations.To address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and imprecision.In the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior accuracy.Extensive testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,respectively.These results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关 键 词：GLAUCOMA fundus images machine learning nature-inspired algorithm SEGMENTATION classification 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]