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Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.

Zhan Wu¹, Gonglei Shi², Yang Chen³

¹School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China.

Artificial Intelligence in Medicine

|September 25, 2020

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A new deep learning model, CF-DRNet, accurately grades diabetic retinopathy (DR) severity from fundus images. This automated tool aids in timely diagnosis and treatment of DR, a leading cause of vision loss.

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a major complication of diabetes, often leading to blindness.
Current DR grading methods are time-consuming and carry risks.
Accurate DR grading is crucial for effective treatment.

Purpose of the Study:

To develop an automated, accurate, and efficient tool for DR severity classification.
To propose a hierarchically Coarse-to-fine network (CF-DRNet) for five-class DR grading.

Main Methods:

A novel Coarse-to-fine network (CF-DRNet) using convolutional neural networks (CNNs).
The Coarse Network classifies No DR vs. DR using an attention gate module.
The Fine Network classifies four DR severity stages (mild, moderate, severe NPDR, PDR).

Keywords:

Coarse-to-fine classification Convolutional neural networks Diabetic retinopathy grading Fundus images

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Main Results:

CF-DRNet achieved high performance in classifying five stages of DR severity.
The model demonstrated superior results compared to state-of-the-art methods on public datasets (IDRiD, Kaggle).
Attention gate module effectively highlighted DR lesions and reduced background noise.

Conclusions:

The proposed CF-DRNet offers an efficient and reliable automated solution for DR grading.
This AI tool can significantly aid in clinical diagnosis and management of diabetic retinopathy.
The hierarchical approach effectively captures DR grading complexity.