Automated detection for Retinopathy of Prematurity with knowledge distilling from multi-stream fusion network

Retinopathy of Prematurity (ROP) is a potentially blinding eye disease that primarily occurs in premature infants with low birth weight. It is the main cause of childhood blindness worldwide. Various methods are available for the staging of ROP detection. However, relatively few research has focused on the early-stage detection of ROP and Treatment-Requiring ROP (TR-ROP). Besides, most of the networks proposed in recent research contain tremendous neural network parameters. This study aimed to propose a lightweight TR-ROP detection neural network with knowledge distilling from a multi-stream fusion neural network based on early-stage fundus images. A multi-stream fusion neural network was first trained with high accuracy, then knowledge distillation was used to transfer knowledge from it to a lighter model to be suitable for deployment into an embedded ROP detection device. Experiments were conducted by using a five-fold cross-validation with a dataset consisting entirely of early-stage fundus images. The proposed network could achieve promising results, with accuracy, sensitivity, and specificity of 0.9734, 0.9456, and 0.9823, in ROP detection, and 0.9222, 0.9516, and 0.8571, in TR-ROP detection, which is superior to the existing state-of-the-art methods.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Retinopathy of Prematurity (ROP) is a potentially blinding eye disease that primarily occurs in premature infants. This study proposes a lightweight TR-ROP detection neural network trained with knowledge distilling from a multi-stream fusion neural network based on early-stage fundus images. Experimental results show that the proposed network achieves promising accuracy, sensitivity, and specificity in ROP and TR-ROP detection, surpassing existing state-of-the-art methods.