Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder because of damage to the eye's retina which can affect babies born prematurely. Screening of ROP is essential for early detection and treatment. This is a laborious and manual process which requires trained physician performing dilated ophthalmological examination which can be subjective resulting in lower diagnosis success for clinically significant disease. Automated diagnostic methods can assist ophthalmologists increase diagnosis accuracy using deep learning. Several research groups have highlighted various approaches. This paper proposes the use of new novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy. The evaluations show that these novel methods in comparison to traditional imaging processing contribute to higher accuracy in classifying Plus disease, Stages of ROP and Zones. We achieve accuracy of 97.65% for Plus disease, 89.44% for Stage, 90.24% for Zones

翻译：早产儿视网膜病变（ROP）是一种因视网膜损伤可能导致失明的眼部疾病，常见于早产婴儿。ROP筛查对于早期发现和治疗至关重要。这一过程耗时且依赖人工操作，需要经过培训的医生进行散瞳眼科检查，该方法具有主观性，可能导致临床上显著疾病的诊断成功率较低。基于深度学习的自动化诊断方法可辅助眼科医生提高诊断准确率。多个研究团队已提出不同方案。本文提出采用新型眼底预处理方法，结合预训练迁移学习框架构建混合模型，以实现更高的诊断准确率。评估表明，与传统图像处理方法相比，这些新方法在分类Plus病变、ROP分期及分区方面具有更高的准确性。本方法在Plus病变分类中达到97.65%的准确率，在分期分类中达到89.44%的准确率，在分区分类中达到90.24%的准确率。