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Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion

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Abstract

The early detection of diabetic retinopathy is crucial for preventing blindness. However, it is time-consuming to analyze fundus images manually, especially considering the increasing amount of medical images. In this paper, we propose an automatic diabetic retinopathy screening method using color fundus images. Our approach consists of three main components: edge-guided candidate microaneurysms detection, candidates classification using mixed features, and diabetic retinopathy prediction using fused features of image level and lesion level. We divide a screening task into two sub-classification tasks: 1) verifying candidate microaneurysms by a naive Bayes classifier; 2) predicting diabetic retinopathy using a support vector machine classifier. Our approach can effectively alleviate the imbalanced class distribution problem. We evaluate our method on two public databases: Lariboisìere and Messidor, resulting in an area under the curve of 0.908 on Lariboisìere and 0.832 on Messidor. These scores demonstrate the advantages of our approach over the existing methods.

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Authors and Affiliations

  1. College of Literature and Journalism, Central South University, Changsha, 410083, China

    Cheng-Zhang Zhu & Ya-Long Xiao

  2. Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment, Changsha, 410083, China

    Cheng-Zhang Zhu, Rong Hu, Bei-Ji Zou, Rong-Chang Zhao, Chang-Long Chen & Ya-Long Xiao

  3. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Rong Hu, Bei-Ji Zou, Rong-Chang Zhao & Chang-Long Chen

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  1. Cheng-Zhang Zhu
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  2. Rong Hu
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Correspondence to Ya-Long Xiao.

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Zhu, CZ., Hu, R., Zou, BJ. et al. Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion. J. Comput. Sci. Technol. 34, 1307–1318 (2019). https://doi.org/10.1007/s11390-019-1977-x

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  • DOI: https://doi.org/10.1007/s11390-019-1977-x

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