DA-M2Det: An Iris Classification Network for UBM Images

Abstract

Abstract Primary angle closure glaucoma (PACG) is primarily diagnosed by ophthalmologists through morphological analysis of the iris in ultrasonic biomicrocopy(UBM). In recent years, Deep convolutional neural networks (CNNs) show potential for quick category definition in eye disease. According to the characteristics of iris in UBM images, we proposed a network (DenseNet and Attention gate) DA-M2Det to automatic classification iris morphology. Firstly, in the framework of M2Det network, We used the backbone of DenseNet to replace the VGG backbone of M2Det, better extraction of basic feature layers. Secondly, three scales of attention gate (AG) was added to the Thinned U-shape Module (TUM), enable the network to pay more attention to the iris region. Finally, we use the retraining method to further improve the accuracy of iris classification. The classification results of VGG-16, M2Det, ResNet-50 and DA-M2Det networks are compared experimentally. The results show that, in three different iris shapes (including arch, flat and depression), DA-M2Det achieves an average classification accuracy of 85%, which is higher than that of the other three networks. Experimental results show that DA-M2Det can accurately classify irises into three categories, assisting ophthalmologists to quickly diagnose the cause of glaucoma and accurately perform clinical treatment thereby.