IRO Journals
Journal of Artificial Intelligence and Capsule Networks

AI Based Early Childhood Glaucoma Risk Screening System Using Eye Image Analysis

Open Access
Published: 13 May, 2026
Pages: 108-120
  • Muthulakshmi I. , Muthulakshmi I.
    Assistant Professor, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
    Assistant Professor, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
  • Parameshwari V. , Parameshwari V.
    Student, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
  • Mubitha J. , Mubitha J.
    Student, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
  • Vigneshwari K. Vigneshwari K.
    Student, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, VV College of Engineering, Thoothukudi, India
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How to Cite

I., Muthulakshmi, Parameshwari V., Mubitha J., and Vigneshwari K. 2026. “AI Based Early Childhood Glaucoma Risk Screening System Using Eye Image Analysis”. Journal of Artificial Intelligence and Capsule Networks 8 (2): 108-20. https://doi.org/10.36548/jaicn.2026.2.003.
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Keywords

Glaucoma
Eye Image Analysis
Artificial Intelligence
Image Processing
Cup-to-Disc Ratio

Abstract

This study describes an artificial intelligence-inspired, rule-based framework for early detection of risk of developing childhood glaucoma through analysis of the child's retina using colour fundus images. The system will first employ a series of pre-processing techniques on the input fundus image including conversion to greyscale format, noise reduction and contrast enhancement prior to finding the optic disc and optic cup in the images through use of a variety of image processing methods. From these two areas, the vertical Cup-to-disc ratio (CDR), which has been scientifically shown to be an important clinical measurement parameter for assessing the potential presence or future development of glaucoma, is calculated. The CDR calculation can be used to classify an individual as being at low, moderate or high risk of developing childhood glaucoma. The final implementation of the system will be as a web-based application where users can take a digital retinal image using a scanner or upload an existing image for analysis. Experimental results indicate that the designed system will yield quality results that are reliable and efficient for use as an initial screening procedure for childhood glaucoma. The system is also designed to be low-cost and readily accessible so that it can be used in a variety of possible clinical settings such as schools for children, rural healthcare clinics and within telemedicine environments to facilitate early detection of childhood glaucoma. 

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