论文标题
糖尿病性视网膜病分类的最先进的深度学习算法的转换和实施
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy
论文作者
论文摘要
糖尿病性视网膜病（DR）是一种视网膜微血管疾病，在糖尿病患者中出现。博士将继续成为全球失明的主要原因，预计2030年将有1,910万全球诊断出的患者。微型神经瘤，出血，散发和棉花羊毛斑是DR的常见迹象。但是，它们可能很小而难以探测人的眼睛。 DR的早期检测对于有效的临床治疗至关重要。对图像进行分类的现有方法需要大量时间提取和选择，并且其性能受到限制。作为一种新兴的深度学习方法，卷积神经网络（CNN）证明了它们在图像分类任务中的潜力。在本文中，进行了针对DR检测和分类的最先进的CNN的全面实验研究，以确定任务的最佳性能分类器。通过实验评估了五个CNN分类器，即Inception-V3，VGG19，VGG16，RESNET50和IncteionResnETV2。他们根据DR严重性将医学图像分为五个不同的类。由于注释的医学图像有限且不平衡，因此应用数据增强和转移学习技术。实验结果表明，RESNET50分类器具有二进制分类的最佳性能，并且InceptionResnETV2分类器具有多级DR分类的最佳性能。
Diabetic retinopathy (DR) is a retinal microvascular condition that emerges in diabetic patients. DR will continue to be a leading cause of blindness worldwide, with a predicted 191.0 million globally diagnosed patients in 2030. Microaneurysms, hemorrhages, exudates, and cotton wool spots are common signs of DR. However, they can be small and hard for human eyes to detect. Early detection of DR is crucial for effective clinical treatment. Existing methods to classify images require much time for feature extraction and selection, and are limited in their performance. Convolutional Neural Networks (CNNs), as an emerging deep learning (DL) method, have proven their potential in image classification tasks. In this paper, comprehensive experimental studies of implementing state-of-the-art CNNs for the detection and classification of DR are conducted in order to determine the top performing classifiers for the task. Five CNN classifiers, namely Inception-V3, VGG19, VGG16, ResNet50, and InceptionResNetV2, are evaluated through experiments. They categorize medical images into five different classes based on DR severity. Data augmentation and transfer learning techniques are applied since annotated medical images are limited and imbalanced. Experimental results indicate that the ResNet50 classifier has top performance for binary classification and that the InceptionResNetV2 classifier has top performance for multi-class DR classification.
