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Diabetic macular edema grading based on improved Faster R-CNN and MD-ResNet

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Abstract

Diabetic macular edema (DME) is the main cause of visual impairment in diabetic patients. Early detection of DME will significantly reduce the risk of vision loss for the patients. According to the clinical DME grading standard, the positional relationship between Hard Exudates (HEs) and macular center is an important basis for DME grading. Accurate DME grading is thus predicated on properly locating the macular center and segmenting HEs. HEI-MED and E-ophtha EX data sets were tested by the proposed DME grading method, reaching an average accuracy of 94.4% and 87%, respectively. The proposed method was also tested by comparison against other commonly used methods as per its potential to assist doctors in initially screening DME; it was found to not only improve the efficiency of DME detection, but also to save Optical Coherence Tomography medical resources over the other methods tested.

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Funding

This work was supported by National Natural Science Foundation of China (No. 61601325), Tianjin Science and Technology Major Projects and Engineering (No. 17ZXSCSY00060, No. 17ZXHLSY00040), and the Program for Innovative Research Team in University of Tianjin (Grant No. TD13-5034).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, TianGong University, Tianjin, 300387, China

    Jun Wu, Qianqian Zhang, Zhitao Xiao, Fang Zhang, Lei Geng, Yanbei Liu & Wen Wang

  2. Tianjin Key Laboratory of Optoelectronic Detection Technology and System, TianGong University, Tianjin, 300387, China

    Jun Wu, Qianqian Zhang, Zhitao Xiao, Fang Zhang, Lei Geng, Yanbei Liu & Wen Wang

  3. State Intellectual Property Office Patent Office Patent Examination Collaboration Beijing Center, Tianjin, 100160, China

    Mengjia Liu

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  1. Jun Wu
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  2. Qianqian Zhang
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  3. Mengjia Liu
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  4. Zhitao Xiao
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  5. Fang Zhang
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  7. Yanbei Liu
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Correspondence to Zhitao Xiao.

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Wu, J., Zhang, Q., Liu, M. et al. Diabetic macular edema grading based on improved Faster R-CNN and MD-ResNet. SIViP 15, 743–751 (2021). https://doi.org/10.1007/s11760-020-01792-3

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  • DOI: https://doi.org/10.1007/s11760-020-01792-3

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