Automated diagnosis of Retinopathy of prematurity from retinal images of preterm infants using hybrid deep learning techniques

Abstract

SignificanceRetinopathy of prematurity (ROP) is a retinal disease in premature infants, which, when left untreated, can cause permanent blindness. Due to high variability and interobserver inconsistency in the diagnosis of ROP, there is a significant need to develop an automated system for the prediction of ROP. Although several methods have been used in automated diagnosis in ROP, no dedicated models have been described with adequate performance. Objectivesi) To develop a hybrid deep learning network for the prediction of ROP that can be used in the mass screening of infants. ii) To compare the performance of the proposed hybrid model with the Machine Learning classifiers and pre-trained CNN model. MethodsThe hybrid network is trained with 3200 and tested with 800 infant fundus images. Modified MultiResUNet and matched filter with first-order Gaussian derivative are used to segment the retinal vessels from the fundus images. The Gray level co-occurrence matrix (GLCM) and contour features of segmented images are extracted and selected using an embedded feature selection method. The selected features are evaluated using permutation importance and classified using the Random Forest classifier. ResultsThe proposed hybrid model predicts ROP with an accuracy of 94.5%, sensitivity of 94%, and specificity of 93%, surpassing the machine learning classifiers and the pre-trained models with reduced parameters and being computationally inexpensive. ConclusionsThe proposed hybrid model improves the quality of infant care by providing additional diagnostic assistance to clinicians. It also enhances the accessibility to remote healthcare centers for large-scale automated screening systems.