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Proceedings Paper

Automated retinopathy of prematurity screening using deep neural network with attention mechanism
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Paper Abstract

Retinopathy of prematurity (ROP) is an ocular disease which occurs in premature babies and is considered as one of the largest preventable causes of childhood blindness. However, insufficient ophthalmologists are qualified for ROP screening, especially in developing countries. Therefore, automated screening of ROP is particularly important. In this paper, we propose a new ROP screening network, in which pre-trained ResNet18 is taken as backbone and a proposed attention block named Complementary Residual Attention Block (CRAB) and Squeeze-and-Excitation (SE) block as channel attention module are introduced. Our main contributions are: (1) Demonstrating the 2D convolutional neural network model pre-trained on natural images can be fine-tuned for ROP screening. (2) Based on the pre-trained ResNet18, we propose an improved scheme combining which that effectively integrates attention mechanism for ROP screening. The proposed classification network was evaluated on 9794 fundus images from 650 subjects, in which 8351 are randomly selected as training set according to subjects and others are selected as testing set. The results showed that the performance of the proposed ROP screening network achieved 99.17% for accuracy, 98.65% for precision, 98.31% for recall, 98.48% for F1 score and 99.84% for AUC. The preliminary experimental results show the effectiveness of the proposed method.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131321 (10 March 2020); doi: 10.1117/12.2548290
Show Author Affiliations
Yuanyuan Peng, Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Feng Chen, Guangzhou Women And Children Medical Ctr. (China)
Daoman Xiang, Guangzhou Women And Children Medical Ctr. (China)
Xinjian Chen, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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