Chetoui Mohamed, Akhloufi Moulay A
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1966-1969. doi: 10.1109/EMBC44109.2020.9175664.
Diabetic retinopathy (DR) is a medical condition due to diabetes mellitus that can damage the patient retina and cause blood leaks. This condition can cause different symptoms from mild vision problems to complete blindness if it is not timely treated. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet to detect referable diabetic retinopathy (RDR) and vision-threatening DR. Tests were conducted on two public datasets, EyePACS and APTOS 2019. The obtained results achieve state-of-the-art performance and show that the proposed network leads to higher classification rates, achieving an Area Under Curve (AUC) of 0.984 for RDR and 0.990 for vision-threatening DR on EyePACS dataset. Similar performances are obtained for APTOS 2019 dataset with an AUC of 0.966 and 0.998 for referable and vision-threatening DR, respectively. An explainability algorithm was also developed and shows the efficiency of the proposed approach in detecting DR signs.
糖尿病视网膜病变（DR）是一种由糖尿病引起的病症，它会损害患者的视网膜并导致血液渗漏。如果不及时治疗，这种病症会引发从轻微视力问题到完全失明的不同症状。在这项工作中，我们提出使用一种基于名为EfficientNet的最新卷积神经网络的深度学习架构，来检测可转诊糖尿病视网膜病变（RDR）和威胁视力的DR。在两个公共数据集EyePACS和APTOS 2019上进行了测试。获得的结果达到了当前的最佳性能，表明所提出的网络导致更高的分类率，在EyePACS数据集上，RDR的曲线下面积（AUC）达到0.984，威胁视力的DR的AUC达到0.990。对于APTOS 2019数据集也获得了类似的性能，可转诊和威胁视力的DR的AUC分别为0.966和0.998。还开发了一种可解释性算法，展示了所提出方法在检测DR体征方面的有效性。
