用于改善早产儿视网膜病变深度学习分类的新型 Retcam 眼底图像预处理方法
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity
AI总结：
本文针对 Retcam 早产儿视网膜病变图像质量差的问题，提出结合新型眼底预处理与预训练迁移学习的混合模型，在 Plus 病、ROP 分期和分区分类上取得优于传统方法的准确性。
摘要：
早产儿视网膜病变（ROP）是一种可能致盲的眼部疾病，由视网膜损伤引起，可影响早产出生的婴儿。ROP 筛查对于早期发现和治疗至关重要。这是一个费时费力的手动过程，需要经过培训的医生进行散瞳眼科检查，该检查可能具有主观性，导致对具有临床意义的疾病的诊断成功率较低。自动化诊断方法可以利用深度学习帮助眼科医生提高诊断准确性。多个研究小组已经提出了各种方法。采集到的 ROP Retcam 图像质量较差。本文提出使用改进的新型眼底预处理方法，结合预训练迁移学习框架来构建混合模型，以实现更高的诊断准确性。经过训练和验证后，评估结果表明，与传统图像处理方法相比，这些新型方法在分类 Plus 病、ROP 分期和分区方面贡献了更好且在多个方面更高的准确性，与同行论文相比具有优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
