National Institute of Technology, Tiruchirappalli, Tamil Nadu, India.
Phys Eng Sci Med. 2021 Sep;44(3):639-653. doi: 10.1007/s13246-021-01012-3. Epub 2021 May 25.
Eye care professionals generally use fundoscopy to confirm the occurrence of Diabetic Retinopathy (DR) in patients. Early DR detection and accurate DR grading are critical for the care and management of this disease. This work proposes an automated DR grading method in which features can be extracted from the fundus images and categorized based on severity using deep learning and Machine Learning (ML) algorithms. A Multipath Convolutional Neural Network (M-CNN) is used for global and local feature extraction from images. Then, a machine learning classifier is used to categorize the input according to the severity. The proposed model is evaluated across different publicly available databases (IDRiD, Kaggle (for DR detection), and MESSIDOR) and different ML classifiers (Support Vector Machine (SVM), Random Forest, and J48). The metrics selected for model evaluation are the False Positive Rate (FPR), Specificity, Precision, Recall, F1-score, K-score, and Accuracy. The experiments show that the best response is produced by the M-CNN network with the J48 classifier. The classifiers are evaluated across the pre-trained network features and existing DR grading methods. The average accuracy obtained for the proposed work is 99.62% for DR grading. The experiments and evaluation results show that the proposed method works well for accurate DR grading and early disease detection.
眼科专业人员通常使用眼底镜检查来确认糖尿病视网膜病变 (DR) 的发生。早期 DR 检测和准确的 DR 分级对于这种疾病的护理和管理至关重要。本工作提出了一种自动化的 DR 分级方法，其中可以从眼底图像中提取特征，并使用深度学习和机器学习 (ML) 算法根据严重程度进行分类。多路径卷积神经网络 (M-CNN) 用于从图像中提取全局和局部特征。然后，使用机器学习分类器根据严重程度对输入进行分类。该模型在不同的公共可用数据库（IDRiD、Kaggle（用于 DR 检测）和 MESSIDOR）和不同的机器学习分类器（支持向量机 (SVM)、随机森林和 J48）上进行了评估。用于模型评估的指标是假阳性率 (FPR)、特异性、精度、召回率、F1 分数、K 分数和准确性。实验表明，M-CNN 网络与 J48 分类器产生的响应最佳。对分类器进行了评估，包括预训练网络特征和现有的 DR 分级方法。所提出的工作的平均准确率为 99.62%，用于 DR 分级。实验和评估结果表明，该方法在准确的 DR 分级和早期疾病检测方面效果良好。
