@Article{cmes.2025.062301,
AUTHOR = {Jahanzaib Latif , Ahsan Wajahat, Alishba Tahir, Anas Bilal, Mohammed Zakariah, Abeer Alnuaim},
TITLE = {A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {143},
YEAR = {2025},
NUMBER = {1},
PAGES = {539--567},
URL = {http://www.techscience.com/CMES/v143n1/60466},
ISSN = {1526-1506},
ABSTRACT = {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 vector machine (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.},
DOI = {10.32604/cmes.2025.062301}
}