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. Captured ROP Retcam images suffer from poor quality. This paper proposes the use of improved novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy. Once trained and validated, the evaluations showed that these novel methods in comparison to traditional imaging processing contribute to better and in many aspects higher accuracy in classifying Plus disease, Stages of ROP and Zones in comparison to peer papers.

翻译：早产儿视网膜病变（ROP）是一种因视网膜损伤导致的潜在致盲性眼病，可影响早产婴儿。ROP筛查对于早期发现和治疗至关重要，但这一过程费时费力且依赖人工操作，需由专业医师进行散瞳眼科检查，具有主观性，导致临床显著性病变的诊断成功率较低。基于深度学习的自动化诊断方法可辅助眼科医师提升诊断准确性。多个研究团队已提出不同技术方案。然而，采集的ROP Retcam图像存在质量缺陷。本文提出采用改进的新型眼底图像预处理方法，结合预训练迁移学习框架构建混合模型，以实现更高的诊断精度。经过训练与验证，评估结果表明：相较传统图像处理方法，这些新型方法在Plus病变、ROP分期及分区的分类准确率上均有提升，且在多方面优于同类文献报道的水平。