Open Access

ARTICLE

From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification

K. Mithra^1,*, Prem Kumar Santhanam²

1 Independent Researcher, Information and Communication Engineering, Scottsdale, AZ, USA
2 Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, USA

* Corresponding Author: K. Mithra. Email:

Journal of Intelligent Medicine and Healthcare 2026, 4, 99-108. https://doi.org/10.32604/jimh.2026.083110

Received 02 April 2026; Accepted 22 May 2026; Issue published 18 June 2026

Abstract

Objective: To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye, without specialised ophthalmic hardware. Methods: A hybrid framework was designed that fuses deep features from a Convolutional Neural Network (CNN) with five handcrafted Grey-Level Co-occurrence Matrix (GLCM) and intensity descriptors—mean intensity, uniformity, standard deviation, contrast, and energy—extracted from a Hough-circle-localised pupil Region of Interest (ROI). A multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel classifies each image into one of four severity grades: normal, immature, mature, or hypermature cataract. Results: The proposed fused system achieved 95.0% accuracy, 93.8% sensitivity, and 96.1% specificity on an ophthalmologist-labelled test set drawn from 300 images (75 per class) collected at an ophthalmology clinic, outperforming texture-only (88.5%) and CNN-only (91.3%) baselines and surpassing recently published deep learning approaches. Conclusion: The CNN–GLCM–SVM fusion framework provides competitive four-class cataract grading without GPU acceleration or specialised cameras, making it suitable for primary-care and telemedicine deployment in resource-limited settings.

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Keywords

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