An Advanced Approach for Predicting ROP Stages: Deep Learning Algorithms and Belief Function Technique

Abstract

A significant cause of blindness in preterm infants is retinal retinopathy of prematurity (ROP). Early detection and intervention are essential for preventing visual loss. This study proposes an advanced approach for predicting ROP stages using deep learning algorithms and belief function theory. Three steps comprise the suggested technique: image pre-processing, feature extraction using deep learning models, and classification utilizing belief function theory. We used a dataset of 3720 retinal images from premature infants and achieved a classification accuracy of 95.57% for predicting ROP stages. Our results demonstrate the effectiveness of deep learning algorithms and belief function theory in ROP diagnosis. This strategy can increase the efficacy and precision of ROP diagnosis, improving the treatment course for premature infants at risk of vision loss.