Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks

作者：Jiewei Jiang, Liming Wang, Haoran Fu, Erping Long, Yibin Sun, Ruiyang Li, Zhongwen Li, Mingmin Zhu, Zhenzhen Liu, Jingjing Chen, Zhuoling Lin, Xiaohang Wu, Dongni Wang, Xiyang Liu, Haotian Lin · 发表于：Annals of Translational Medicine · 年份：2021 · DOI：10.21037/atm-20-6635 · 被引用次数：14 · 研究领域：Intraocular Surgery and Lenses、Retinopathy of Prematurity Studies、Ophthalmology and Visual Impairment Studies

BACKGROUND: Lens opacity seriously affects the visual development of infants. Slit-illumination images play an irreplaceable role in lens opacity detection; however, these images exhibited varied phenotypes with severe heterogeneity and complexity, particularly among pediatric cataracts. Therefore, it is urgently needed to explore an effective computer-aided method to automatically diagnose heterogeneous lens opacity and to provide appropriate treatment recommendations in a timely manner. METHODS: We integrated three different deep learning networks and a cost-sensitive method into an ensemble learning architecture, and then proposed an effective model called CCNN-Ensemble [ensemble of cost-sensitive convolutional neural networks (CNNs)] for automatic lens opacity detection. A total of 470 slit-illumination images of pediatric cataracts were used for training and comparison between the CCNN-Ensemble model and conventional methods. Finally, we used two external datasets (132 independent test images and 79 Internet-based images) to further evaluate the model's generalizability and effectiveness. RESULTS: Experimental results and comparative analyses demonstrated that the proposed method was superior to conventional approaches and provided clinically meaningful performance in terms of three grading indices of lens opacity: area (specificity and sensitivity; 92.00% and 92.31%), density (93.85% and 91.43%) and opacity location (95.25% and 89.29%). Furthermore, the comparable perfo...