What is it about?

A feature pyramid network and a modified region proposal network are applied to enhance the detection of small objects. The DR-grading model based on the modified R-FCN is evaluated on the Messidor dataset and images provided by the Shanghai Eye Hospital. High sensitivity of 99.39% and specificity of 99.93% are obtained on the hospital data. Moreover, high sensitivity of 92.59% and specificity of 96.20% are obtained on the Messidor dataset. The modified R-FCN lesion-detection model is validated on the hospital dataset and achieves a 92.15% mean average precision. The proposed R-FCN can efficiently accomplish DR grading and lesion detection with high accuracy.

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Why is it important?

Use Object Detect Algorithm of Deep CNN Network to solve medical imaging problems. In this work, we develop a computer-aided retinal image screening system that can perform automated diabetic retinopathy (DR) grading and DR lesion detection in retinal fundus images.


Another method about deep learning application in medical imaging.

Jialiang Wang
East China University of Science and Technology

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This page is a summary of: Automated diabetic retinopathy grading and lesion detection based on the modified R-FCN object detection algorithm, IET Computer Vision, September 2019, the Institution of Engineering and Technology (the IET), DOI: 10.1049/iet-cvi.2018.5508.
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