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Retinopathy of Prematurity (ROP) is a potentially blinding eye disorderbecause 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 alaborious and manual process which requires trained physician performingdilated ophthalmological examination which can be subjective resulting in lowerdiagnosis success for clinically significant disease. Automated diagnosticmethods can assist ophthalmologists increase diagnosis accuracy using deeplearning. Several research groups have highlighted various approaches. CapturedROP Retcam images suffer from poor quality. This paper proposes the use ofimproved novel fundus preprocessing methods using pretrained transfer learningframeworks to create hybrid models to give higher diagnosis accuracy. Oncetrained and validated, the evaluations showed that these novel methods incomparison to traditional imaging processing contribute to better and in manyaspects higher accuracy in classifying Plus disease, Stages of ROP and Zones incomparison to peer papers.

Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity