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Feature-transfer network and local background suppression for microaneurysm detection

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Abstract

Microaneurysm (MA) is the earliest lesion of diabetic retinopathy (DR). Accurate detection of MA is helpful for the early diagnosis of DR. In this paper, an efficient approach is proposed to detect MA, based on feature-transfer network and local background suppression. In order to reduce noise, a feature-distance-based algorithm is proposed to suppress local background. The similarity matrix of feature distances is calculated to measure the difference between background noise and retinal objects. Moreover, a feature-transfer network is proposed to detect MAs with imbalanced data. For each training process, the optimized weights and bias are transferred to the next training, until the optimal network is generated. Experimental results demonstrate that the proposed approach can accurately detect subtle MAs surrounded by complex background. Furthermore, the sensitivity values on the public datasets are up to 98.3%, 100%, 99.3%, 100%, 96.5%, respectively. The proposed approach outperforms the state-of-the-arts, in terms of the competition performance measure score.

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Acknowledgements

This work was supported by the National Key R&D Program of China under Grant No. 2018YFB1003201. It was also supported in part by the National Natural Science Foundation of China under Grant Nos. 61902078 and 61702114. It was supported in part by Key-Area R&D Program of Guangdong under Grant No. 2018B010107003, by Natural Science Foundation of Guangdong, China, under Grant Nos. 2018B030311007 and 2020A1515011361.

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology (GDUT), Guangzhou, China

    Xinpeng Zhang, Jigang Wu, Min Meng & Yifei Sun

  2. School of Automation, Guangdong University of Technology (GDUT), Guangzhou, China

    Weijun Sun

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  1. Xinpeng Zhang
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Correspondence to Jigang Wu.

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Zhang, X., Wu, J., Meng, M. et al. Feature-transfer network and local background suppression for microaneurysm detection. Machine Vision and Applications 32, 1 (2021). https://doi.org/10.1007/s00138-020-01119-9

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