[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127232-en":3,"doc-seo-127232-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127232,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine Learning and Artifact Convolutional Neural Network based Approach for Early-Stage Glaucoma Prediction","Early-stage glaucoma detection is essential to prevent irreversible vision loss, yet conventional screening often struggles with accuracy, efficiency, and timely expert-dependent assessment. This research presents a Machine Learning and Artifact Convolutional Neural Network-based approach designed to improve diagnostic precision through deep learning. The model is trained and evaluated on a structured dataset using accuracy, precision, recall, and F1-score, with results validated by a confusion matrix to distinguish glaucoma from non-glaucoma cases reliably. Future enhancements target larger datasets, better generalization, and clinical integration for dependable automated screening.","Machine Learning and Artifact Convolutional Neural Network based Approach for Early-Stage Glaucoma Prediction  \nSEEJPH Volume XXV ,2024, ISSN: 2197-5248; Posted:25-12-24  \nMachine Learning and Artifact Convolutional Neural Network based Approach for Early-Stage Glaucoma Prediction  \nArjun Kotwal1, Isha Yadav2, Pinkee3, B Murali Krishna4, Monika Mahajan5, Rashmi Tiwari6, Amjed Khan Bhatti7  \n1Sr. Assistant Professor, Department of Computer Applications, PSPS Govt. College for Women, Gandhi Nagar,  \nJammu (J&K), INDIA Email: [kotwalarjun@gmail.com](kotwalarjun@gmail.com)  \n2Assistant Professor, NIMS School of Computing and Artificial Intelligence, NIET, NIMS University, Jaipur  \n(Rajasthan), INDIA Email: [isha.24211@gmail.com](isha.24211@gmail.com)  \n3Assistant Professor, Department Computer Science and Engineering, NGF College of Engineering and  \nTechnology, Palwal (Haryana), [INDIA Email:](INDIA Email: pinkeesingh15@gmail.com)[ p](INDIA Email: pinkeesingh15@gmail.com)[inkeesingh15@gmail.com](INDIA Email: pinkeesingh15@gmail.com)  \n4Research Scholar (Computer Science and Engineering), School of Engineering and Technology  \nShri Venkateshwara University, Gajraula (UP), INDIA Email: [banalamurali05@gmail.com](banalamurali05@gmail.com)  \n5Assistant Professor, Department of Computer Science, Govt SPMR College of Commerce, Cluster University of  \nJammu, Jammu( J&K), INDIA, [Email: mahajan.monika0203@gmail.com](Email: mahajan.monika0203@gmail.com)  \n6Research Scholar (Computer Science), School of Engineering and Technology  \nShri Venkateshwara University, Gajraula (UP) [INDIA Email:tiwari.rasmi087@gmail.com](INDIA Email:tiwari.rasmi087@gmail.com)  \n7Assistant Professor, Department of Computer Applications andIT, PSPS Govt. College for Women  \nGandhinagar, Jammu (J&K), INDIA Email: [amjedbhatti07@gmail.com](amjedbhatti07@gmail.com)  \nKEYWORDS  \nCNN, SVM, Glaucoma, CDR, Machine Learning  \nABSTRACT  \nEarly detection of glaucoma is crucial for preventing irreversible vision loss, yet traditional diagnostic methods often face challenges in accuracy and efficiency. This research proposes a Machine Learning and Artifact Convolutional Neural Network-based approach for early-stage glaucoma prediction, leveraging deep learning techniques to enhance diagnostic precision. The model is trained and evaluated using a well-structured dataset, ensuring robust performance through metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed approach outperforms conventional classification methods, achieving superior accuracy while minimizing false positives and false negatives. A comprehensive analysis using a confusion matrix further validates its reliability in distinguishing between glaucoma and non-glaucoma cases. The study highlights the potential of AI-driven solutions in ophthalmology, offering a promising tool for automated, efficient, and early glaucoma detection. Future work may focus on expanding datasets, improving model generalization, and integrating real-world clinical applications to enhance diagnostic reliability  \n1. INTRODUCTION  \nGlaucoma is the second leading cause of blindness worldwide, affecting approximately 79 million individuals, with an estimated 11.9 million cases in India alone. This neurodegenerative disease is characterized by progressive damage to the optic nerve, often resulting from elevated intraocular pressure (IOP) . If left undiagnosed and untreated, glaucoma can lead to irreversible vision loss. One of the major challenges in managing glaucoma is its asymptomatic nature in the early stages, which causes a significant number of cases to remain undetected until substantial and often irreversible vision impairment occurs [10] . This issue is particularly concerning for individuals over the age of 60, as they are at a higher risk of developing the disease. Traditional diagnostic methods, such as tonometry, visual field tests, and manual cupto-disc ratio (CDR) measurem","cbCaifrMdWfNbie4","https://ap.wps.com/l/cbCaifrMdWfNbie4","pdf",316446,2,1,11,"English","en",105,"# Introduction\n## Glaucoma background and challenges in early detection\n## Traditional diagnostic methods and limitations\n## AI-driven approach with ACNN\n## Proposed methodology: preprocessing, segmentation, feature extraction","[{\"question\":\"Why is early detection of glaucoma critical?\",\"answer\":\"Glaucoma can cause progressive optic nerve damage and irreversible vision loss, while early-stage disease is often asymptomatic and remains undiagnosed without timely intervention.\"},{\"question\":\"What traditional methods are commonly used for glaucoma screening?\",\"answer\":\"Common approaches include tonometry, visual field tests, and manual cup-to-disc ratio (CDR) measurements from retinal fundus images, which often require expert skill and can be time-consuming.\"},{\"question\":\"How does the proposed ACNN-based approach support early-stage prediction?\",\"answer\":\"The method uses preprocessing to reduce noise, segmentation to identify optic disc and optic cup regions, and feature extraction to compute CDR for classification, with performance validated using accuracy metrics and a confusion matrix.\"}]","Machine Learning and Artifact Convolutional Neural Network based Approach for Early-Stage Glaucoma Prediction | 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is early detection of glaucoma critical?","Question",{"text":76,"@type":77},"Glaucoma can cause progressive optic nerve damage and irreversible vision loss, while early-stage disease is often asymptomatic and remains undiagnosed without timely intervention.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What traditional methods are commonly used for glaucoma screening?",{"text":81,"@type":77},"Common approaches include tonometry, visual field tests, and manual cup-to-disc ratio (CDR) measurements from retinal fundus images, which often require expert skill and can be time-consuming.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed ACNN-based approach support early-stage prediction?",{"text":85,"@type":77},"The method uses preprocessing to reduce noise, segmentation to identify optic disc and optic cup regions, and feature extraction to compute CDR for classification, with performance validated using accuracy metrics and a confusion 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