发表机构
Seidenberg School of Computer Science and Information Systems, Pace University（佩斯大学计算机科学与信息系统塞登伯格学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在开发低成本白内障严重程度自动分类系统，设计CNN与GLCM特征融合的混合框架，提取相关特征并经多类SVM分类。该系统在测试集上表现出色，优于基线和其他方法，适用于资源有限环境的医疗部署。
AI 中文摘要
目的：开发一种低成本的自动白内障严重程度分类系统，可基于标准消费级眼部彩色照片运行，无需专业眼科硬件。方法：设计了一种混合框架，融合来自卷积神经网络（CNN）的深度特征与从霍夫圆定位的瞳孔感兴趣区域（ROI）提取的五个手工制作的灰度共生矩阵（GLCM）和强度描述符——平均强度、均匀性、标准差、对比度和能量。使用具有径向基函数（RBF）核的多类支持向量机（SVM）将每个图像分类为四个严重程度等级之一：正常、未成熟、成熟或过熟白内障。结果：所提出的融合系统在从眼科诊所收集的300张图像（每类75张）的眼科医生标记测试集上达到了95.0%的准确率、93.8%的灵敏度和96.1%的特异性，优于仅纹理（88.5%）和仅CNN（91.3%）的基线，并超过了最近发表的深度学习方法。结论：CNN - GLCM - SVM融合框架在无需GPU加速或专业相机的情况下提供了有竞争力的四类白内障分级，适用于资源有限环境中的初级保健和远程医疗部署。
英文摘要
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.
Comments10 pages
Journal refJournal of Intelligent Medicine and Healthcare 2026, 4, 99-108
