ArXiv Preprint
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
Sajid Rahim, Kourosh Sabri, Anna Ells, Alan Wassyng, Mark Lawford, Linyang Chu, Wenbo He
2023-02-06
