Abstract
Diabetic retinopathy (DR) is one of the major diseases causing blindness, and microaneurysms in the fundus are the first reliable lesions in its early stage. This paper proposes an object detection method for microaneurysms based on R-CNN, which consists of five steps: image preprocessing, candidate region generation, feature extraction, classification and non-maximal suppression. First, a fundus image preprocessing method and a candidate region generation algorithm for microaneurysms are proposed. Then, the VGG16 network is trained using the transferred fine-tuning model to extract features from candidate samples. Finally, real aneurysms are selected from candidate regions by a classifier. The experimental results in the internationally published database e-ophtha show that the proposed method outperforms other known related methods on the FROC indicator.
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Acknowledgment
This research was supported by the National Nature Science Foundation of China (No. 61603197 and No. 61772284).
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Wang, Z., Chen, KJ., Zhang, L. (2019). A R-CNN Based Approach for Microaneurysm Detection in Retinal Fundus Images. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_19
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