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

A hybrid CNN-GLCM-SVM fusion framework achieved 95.0% accuracy, 93.8% sensitivity, and 96.1% specificity in automated four-class cataract severity classification from standard consumer-grade eye photographs, outperforming CNN-only (91.3%) and texture-only (88.5%) baselines. The system, developed by researchers using 300 ophthalmologist-labelled images, requires no GPU or specialised hardware, enabling low-cost deployment in primary-care and telemedicine settings.

arXiv:2607.18349v1 Announce Type: new 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.