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

TL;DR

This paper introduces novel preprocessing techniques for Retcam images using transfer learning to enhance deep learning-based classification of Retinopathy of Prematurity, leading to higher diagnostic accuracy.

Contribution

It proposes new fundus image preprocessing methods with transfer learning frameworks to improve ROP classification accuracy over traditional approaches.

Findings

Improved accuracy in classifying Plus disease

Enhanced detection of ROP stages and zones

Outperforms peer methods in diagnostic precision

Abstract

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.