@Article{jimh.2026.083110,
AUTHOR = {K. Mithra, Prem Kumar Santhanam},
TITLE = {From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification},
JOURNAL = {Journal of Intelligent Medicine and Healthcare},
VOLUME = {4},
YEAR = {2026},
NUMBER = {1},
PAGES = {99--108},
URL = {http://www.techscience.com/JIMH/v4n1/67759},
ISSN = {2837-634X},
ABSTRACT = { <b>Objective:</b> To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye, without specialised ophthalmic hardware. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> 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.},
DOI = {10.32604/jimh.2026.083110}
}