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Automatic arteriosclerotic retinopathy grading using four-channel with image merging
Shuo Gao1, Li Gao2, Xiongwen Quan1
1College of Artificial Intelligence, Nankai University, Tianjin, China.
Insights
This study introduces a novel automated method for grading arteriosclerosis retinopathy using convolutional neural networks. The new approach significantly improves detection accuracy, offering a more efficient alternative to manual assessment.
Area of Science:
- Ophthalmology and Medical Imaging
- Artificial Intelligence in Healthcare
- Cardiovascular Disease Research
Background:
- Arteriosclerosis retinopathy is a serious complication of hypertension, posing a significant threat to human health.
- Current manual assessment of arteriosclerosis retinopathy is labor-intensive, time-consuming, and costly.
- There is a critical need for automated methods for accurate and efficient grading of arteriosclerosis retinopathy.
Purpose of the Study:
- To develop a novel automated method for grading arteriosclerosis retinopathy using convolutional neural networks (CNNs).
- To enhance feature extraction for fundus blood vessel analysis through image merging and contour enhancement.
- To improve the accuracy and efficiency of arteriosclerosis retinopathy detection and classification.
Main Methods:
- A novel feature extraction scheme involving image merging for contour enhancement was developed.
- Adaptive threshold processing was used to generate a new contour channel, merged with the original fundus image.
- A pre-trained CNN with transfer learning and Kaiming initialization was employed, incorporating ArcLoss for improved classification.
Main Results:
- The proposed method achieved an accuracy of 65.354% for arteriosclerosis retinopathy grading.
- This accuracy represents an improvement of nearly 4% compared to existing methods.
- The method obtained a Kappa score of 0.508, indicating good agreement in grading.
Conclusions:
- The developed CNN-based method demonstrates superior performance in arteriosclerosis retinopathy grading.
- The approach offers a valuable and efficient tool for automated detection and grading of the condition.
- This automated system has the potential to aid clinicians in managing hypertensive retinopathy.
Background And Objective:
Arteriosclerosis can reflect the severity of hypertension, which is one of the main diseases threatening human life safety. But Arteriosclerosis retinopathy detection involves costly and time-consuming manual assessment. To meet the urgent needs of automation, this paper developed a novel arteriosclerosis retinopathy grading method based on convolutional neural network.
Methods:
Firstly, we propose a good scheme for extracting features facing the fundus blood vessel background using image merging for contour enhancement. In this step, the original image is dealt with adaptive threshold processing to generate the new contour channel, which merge with the original three-channel image. Then, we employ the pre-trained convolutional neural network with transfer learning to speed up training and contour image channel parameter with Kaiming initialization. Moreover, ArcLoss is applied to increase inter-class differences and intra-class similarity aiming to the high similarity of images of different classes in the dataset.
Results:
The accuracy of arteriosclerosis retinopathy grading achieved by the proposed method is up to 65.354%, which is nearly 4% higher than those of the exiting methods. The Kappa of our method is 0.508 in arteriosclerosis retinopathy grading.
Conclusions:
An experimental study on multiple metrics demonstrates the superiority of our method, which will be a useful to the toolbox for arteriosclerosis retinopathy grading.
