From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification
A low-cost automated cataract severity classification system using standard consumer-grade eye photos achieves 95.0% accuracy by fusing CNN deep features with five handcrafted GLCM and intensity descriptors via SVM, without GPU or specialized cameras, suitable for primary care and telemedicine in resource-limited settings.
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[Submitted on 20 Jul 2026]
Title:From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification
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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.
Comments: 10 pages
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET)
Cite as: arXiv:2607.18349 [cs.CV]
(or arXiv:2607.18349v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.18349
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Journal of Intelligent Medicine and Healthcare 2026, 4, 99-108
Related DOI:
https://doi.org/10.32604/jimh.2026.083110
DOI(s) linking to related resources
Submission history
From: Mithra K [view email] [v1] Mon, 20 Jul 2026 08:52:39 UTC (3,021 KB)
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