Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network

Abstract

Aggressive Posterior Retinopathy of Prematurity (AP-ROP) is a retinal pathology characterized by severe vasodilation and distortion of the posterior pole of the retina. It may lead to blindness if it is not diagnosed and treated in time. Therefore, early diagnosis of AP-ROP plays a nontrivial role in reducing the blindness rate in children. However, the traditional automated AP-ROP diagnosis methods are based on machine learning with segmentation, where the accuracy is highly dependent on the vessel segmentation. To solve this issue, we propose an approach with two deep convolution networks to automatically diagnose AP-ROP. Specifically, the first network identifies whether the image has the presence of ROP, and the second network divides the ROP images into Regular ROP and AP-ROP. Experimental results show that our proposed method can achieve quite promising AP-ROP diagnosis performance and the transfer learning technique can further boost the automated diagnosis performance.