{"slug": "from-pixel-to-prognosis-convolutional-and-glcm-feature-fusion-for-automated-four", "title": "From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification", "summary": "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.", "body_md": "arXiv:2607.18349v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/from-pixel-to-prognosis-convolutional-and-glcm-feature-fusion-for-automated-four", "canonical_source": "https://arxiv.org/abs/2607.18349", "published_at": "2026-07-22 04:00:00+00:00", "updated_at": "2026-07-22 04:13:26.890348+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision", "ai-research"], "entities": ["Convolutional Neural Network", "Support Vector Machine", "Grey-Level Co-occurrence Matrix", "Radial Basis Function"], "alternates": {"html": "https://wpnews.pro/news/from-pixel-to-prognosis-convolutional-and-glcm-feature-fusion-for-automated-four", "markdown": "https://wpnews.pro/news/from-pixel-to-prognosis-convolutional-and-glcm-feature-fusion-for-automated-four.md", "text": "https://wpnews.pro/news/from-pixel-to-prognosis-convolutional-and-glcm-feature-fusion-for-automated-four.txt", "jsonld": "https://wpnews.pro/news/from-pixel-to-prognosis-convolutional-and-glcm-feature-fusion-for-automated-four.jsonld"}}