论文标题
使用深卷积神经网络的自动糖尿病性视网膜病变分级
Automated Diabetic Retinopathy Grading using Deep Convolutional Neural Network
论文作者
论文摘要
糖尿病性视网膜病是一个全球健康问题，影响了全球1亿个人，在接下来的几十年中，这些事件有望达到流行病的比例。糖尿病性视网膜病是一种微妙的眼科疾病，可能会导致突然的不可逆视力丧失。考虑到眼底摄影视网膜图像的视觉复杂性，早期的糖尿病性视网膜病变诊断对人类专家来说可能具有挑战性。但是，糖尿病性视网膜病的早期检测可以显着改变严重的视力丧失问题。计算机辅助检测系统准确检测糖尿病性视网膜病的能力使研究人员普及了。在这项研究中，我们使用了具有多种修改的预训练的Densenet121网络，并在Aptos 2019数据集中进行了培训。提出的方法在早期检测中优于其他最先进的网络，并且达到了96.51％的糖尿病性视网膜病的严重程度分级的精度，用于多标签分类，并实现了单层分类方法的94.44％精度。此外，据报道，我们网络的精确度，召回，F1得分和二次加权Kappa分别为86％，87％，86％和91.96％。我们提出的架构同时非常简单，准确且有效地涉及计算时间和空间。
Diabetic Retinopathy is a global health problem, influences 100 million individuals worldwide, and in the next few decades, these incidences are expected to reach epidemic proportions. Diabetic Retinopathy is a subtle eye disease that can cause sudden, irreversible vision loss. The early-stage Diabetic Retinopathy diagnosis can be challenging for human experts, considering the visual complexity of fundus photography retinal images. However, Early Stage detection of Diabetic Retinopathy can significantly alter the severe vision loss problem. The competence of computer-aided detection systems to accurately detect the Diabetic Retinopathy had popularized them among researchers. In this study, we have utilized a pre-trained DenseNet121 network with several modifications and trained on APTOS 2019 dataset. The proposed method outperformed other state-of-the-art networks in early-stage detection and achieved 96.51% accuracy in severity grading of Diabetic Retinopathy for multi-label classification and achieved 94.44% accuracy for single-class classification method. Moreover, the precision, recall, f1-score, and quadratic weighted kappa for our network was reported as 86%, 87%, 86%, and 91.96%, respectively. Our proposed architecture is simultaneously very simple, accurate, and efficient concerning computational time and space.
