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Title: :  Glaucoma prediction using machine learning
PaperId: :  26526
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26526
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Prof. Vinutha NVidya Vikas Institute of Engineering & Technology, karnataka, India.
Pranathi HS Vidya Vikas Institute of Engineering & Technology, karnataka, India.
Sri Raksha RK Vidya Vikas Institute of Engineering & Technology, karnataka, India.
Ameesha BC Vidya Vikas Institute of Engineering & Technology, karnataka, India.
Sujan PR Vidya Vikas Institute of Engineering & Technology, karnataka, India.

Abstract

Computer Science Engineering
Glaucoma, Optic nerve, Retinal fundus images, Blindness, Ophthalmology, Machine Learning, Deep Learning, MobileNetV2, Google Colab, Softmax, python, Screening, Data bias
Glaucoma is a progressive and chronic eye disease that causes damage to the optic nerve, which may result in irreversible blindness if diagnosed and treated too late. Conventional diagnostic procedures need specialized hardware and clinical knowledge, which are not always available in all areas. To overcome this, we introduce an automated glaucoma detection system using deep learning, which can process retinal fundus photographs and categorize them as normal and stages of glaucoma advanced with high accuracy. The system leverages the use of Convolutional Neural Networks (CNNs), optimized for visual pattern classification tasks, to extract and analyze glaucoma-related features such as optic disc cupping and thinning of the nerve fibers. Before images are classified, they go through preprocessing procedures such as resizing, pixel normalization, and contrast enhancement to enhance model performance. The trained model gives classification probabilities, which are displayed as bar charts for improved interpretability. The solution is incorporated within a web-based platform developed with IJIUStreamlit, offering a user-friendly interface for image upload, result viewing, and medical recommendations. An AI-driven chatbot also helps users interpret the results, promotes regular checkups, and advises them to get immediate attention when needed. The system supports remote screening, making it highly applicable in rural and underserved healthcare settings. Experimental evaluation demonstrates the model’s strong performance, outperforming several baseline techniques and achieving reliable classification even with noisy or lower quality images. By combining deep learning, intuitive UI, and intelligent assistance, the system enhances early detection capabilities and supports clinical workflows for ophthalmologists and general healthcare providers.

Citations

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IJARIIE Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR . "Glaucoma prediction using machine learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 1467-1472
MLA Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR . "Glaucoma prediction using machine learning." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 1467-1472.
APA Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , & Sujan PR . (2025). Glaucoma prediction using machine learning. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 1467-1472.
Chicago Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR . "Glaucoma prediction using machine learning." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 1467-1472.
Oxford Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR . 'Glaucoma prediction using machine learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 1467-1472. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Glaucoma_prediction_using_machine_learning_ijariie26526.pdf (Accessed : ).
Harvard Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR . (2025) 'Glaucoma prediction using machine learning', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 1467-1472IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Glaucoma_prediction_using_machine_learning_ijariie26526.pdf (Accessed : )
IEEE Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR , "Glaucoma prediction using machine learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 1467-1472, May-Jun 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Glaucoma_prediction_using_machine_learning_ijariie26526.pdf [Accessed : ].
Turabian Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR . "Glaucoma prediction using machine learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver Prof. Vinutha N, Pranathi HS , Sri Raksha RK , Ameesha BC , and Sujan PR . Glaucoma prediction using machine learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 1467-1472. Available from: https://ijariie.com/AdminUploadPdf/Glaucoma_prediction_using_machine_learning_ijariie26526.pdf
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