Using Novel Fundus Image Preprocessing to Improve the Classification of Retinopathy of Prematurity (ROP) Using Deep Learning

Retinopathy of Prematurity (ROP) is a condition which can affect babies born prematurely. It is a potentially blinding eye disorder as a result of damage to the eye's retina. Tortuosity, presence and intensity of demarcation line are key indicators of ROP. Screening of ROP is a laborious and manual process which requires a trained physician performing a dilated ophthalmology examination. Automated diagnostic methods using Artificial Intelligence (AI) can assist ophthalmologists increase diagnosis accuracy using ROP specific features in the patient's digital retina image. For clinical use, pediatric fundus images captured using digital camera such as RetCam [1] are challenged with image quality that reduces visibility of retinal features and therefore must be pre-processed. This paper presents two improved image pre-processing methods that blend traditional and restoration methods to enhance retinal features, making the images more effective for clinical use and AI for diagnosing ROP. These new methods demonstrated improved CNN accuracy compared to traditional image pre-processing on our ROP RetCam dataset. Using ResNet50 and InceptionResNetV2 classifiers, the best results for ROP classification were as follows: Plus Disease: Accuracy 0.98, sensitivity 0.98, specificity $\mathbf{1. 0}$, precision $\mathbf{1. 0 0}$, F1-score $\mathbf{0. 9 8}$. Stages: Accuracy 0.92, sensitivity 0.72, specificity 0.96, precision 0.79, F1-score 0.76. Zones: Accuracy 0.90, sensitivity 0.85, specificity 0.93, precision 0.85, F1 -score 0.85. These results match or surpass those of comparable studies using limited data. This is the first known application of restoration-based image pre-processing for ROP RetCam images, showing enhanced effectiveness in ROP classification.