用普通照片融合深度与纹理特征，自动分级白内障严重程度
From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification
- 结合CNN深层特征与手工提取的GLCM纹理特征
- 在300张照片上达95%准确率，优于纯深度或纯纹理方法
- 无需高端设备，适合基层医疗和远程诊疗
目标：开发一种基于普通消费级彩色眼图的低成本自动化白内障严重程度分类系统，无需专业眼科设备。方法：设计了一种混合框架，将卷积神经网络（CNN）的深层特征与从霍夫圆定位的瞳孔感兴趣区域提取的五种手工纹理特征（灰度共生矩阵GLCM及均值、均匀性、标准差、对比度、能量）融合，再通过带有径向基函数核的多类支持向量机（SVM）将图像分类为四类：正常、未成熟、成熟、过熟白内障。结果：该融合系统在由300张图像（每类75张）组成的医生标注测试集上达到95.0%准确率、93.8%敏感度、96.1%特异度，优于仅用纹理（88.5%）和仅用CNN（91.3%）的基线模型，并超越近期已发表的深度学习方法。结论：CNN-GLCM-SVM融合框架在无GPU加速和专用相机条件下实现有竞争力的四类白内障分级，适用于资源匮乏地区的初级医疗与远程诊疗部署。
原文摘要 · Abstract (English)
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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