Advances in Early Detection and Monitoring of Retinopathy in Preterm Infants Using CNN and MLP Models

This research showcases significant advancements in the timely identification and monitoring of retinopathy among preterm infants through the integration of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) models. We eradicate the necessity for subjective assessments by ensuring a precise and automated diagnosis through the implementation of these contemporary models. The CNN is capable of extracting robust features from retinal images, and the MLP model enhances the predictive precision and monitoring functionalities even further. By integrating these models, our approach enhances the precision and velocity of retinopathy diagnosis, thereby resulting in improved therapeutic outcomes. The proposed framework significantly improves neonatal care by incorporating cutting-edge technology that enables continuous and advanced detection of retinopathy in premature infants.