Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manjunath, Chinthakunta; Sasi, Archana; Chowdary Ch, Smitha; Sharon Roji Priya, C.; Naick, B. Raveendra; Macherla, Harshini; Lella, Kranthi Kumar Title A name given to the resource Enhancing Glaucoma Detection in Fundus Images: A ResNet based Segmentation and Advanced ML Algorithms with Duck Pack Optimizer Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Research Journal of Multidisciplinary Technovation;Volume;7;Issue;2;pp.108-120 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.54392/irjmt2529" target="_blank" rel="noreferrer noopener">https://doi.org/10.54392/irjmt2529</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001519610?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001519610?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Manjunath C., Department of Computer Science and Engineering (AI &amp; ML), Vidyavardhaka College of Engineering, Mysore, India; Sasi A., Department of CSE, Faculty of Engineering and Technology, Jain University, Kannagapura Rd, Karnataka, Bengaluru, 562112, India; Chowdary Ch S., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, India; Sharon Roji Priya C., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Naick B.R., Department of CSE (AI&amp;ML), School of Computing, Mohan Babu University, Tirupati, India; Macherla H., Department of Information Technology, MLR Institute of Technology, Hyderabad, 500043, India; Lella K.K., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India Description An account of the resource Untreated glaucoma, a chronic eye illness, can cause irreversible vision loss if not caught early. The condition begins with abnormalities in the eye's drainage flow, leading to a rise in intraocular pressure. As the disease progresses, the optic nerve head deteriorates, resulting in vision loss. Ophthalmologists need extensive training and expertise to interpret findings accurately during medical follow-ups to examine the retina. To address this challenge, deep learning-based algorithms have been developed to screen for and diagnose glaucoma using images of the optic nerve, retinal structures, and retinal fundus. This research explores the use of classification and segmentation algorithms based on ResNet to identify glaucoma in fundus images. We fine-tuned the classifier using the DuckPack optimizer and employed XGBoost, LightGBM, and CatBoost algorithms for classification. The results were promising. The segmentation model based on ResNet effectively extracted features, aiding the classification models in accurately identifying glaucoma. All three algorithms performed admirably, though further fine-tuning is needed to determine the best one. Enhancing the model's performance was straightforward after using the DuckPack optimizer for fine-tuning. This study highlights the promising applications of deep learning and sophisticated machine learning algorithms in glaucoma detection. Its findings could inform the development of future diagnostic tools. The Author(s) 2025. Subject The topic of the resource Catboost; Duck Pack Optimizer; Fundus Images; Glaucoma Detection; Lightgbm; Resnet Based Segmentation; Xgboost Publisher An entity responsible for making the resource available Asian Research Association Relation A related resource ISSN: 25821040; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online