A novel lesion detection algorithm based on multi-scale input convolutional neural network model for diabetic retinopathy

Objective

To propose a multi-scale convolutional neural network (CNN) based lesions detection method of fundus image, and evaluate its application in diabetic retinopathy (DR) assisted diagnosis.

Methods

A multi-scale CNN based on lesions detection method of fundus image was proposed.Compared with the existing detection methods, the problem of poor robustness based on threshold segmentation and morphological segmentation was overcome.The idea of multi-scale grids detection without relying on manual pixel-by-pixel labeling was adopted in this algorithm, and the detection performance of small lesions was significantly improved.In addition, multiple DR lesions with high accuracy could be detected by the proposed loss function under the condition of weak labels and small data sets.

Results

At the level of lesions, the sensitivity and specificity of hard exudation lesions detection were 92.17% and 97.17%, respectively.Compared with single-scale method, the sensitivity and accuracy of multi-scale method proposed in this paper increased by 7.41% and 5.02%, respectively, and compared with other algorithm using the same public dataset IDRiD, the specificity of this algorithm increased by 55.82%.This method could effectively detect the lesions in fundus images, and could give the basic range of the lesions.The average detection time of fundus images with a large number of lesions was 1.59 seconds.

Conclusions

The DR lesions in the fundus image can be quickly and reliably identified, the location information of the lesions can be marked, and the influence of subjective factors can be reduced by using this algorithm, and it can be used to assist the clinician to conduct more effectively.

Artificial intelligence; Diabetic retinopathy/diagnosis; Fundus color photograph; Multi-scale convolutional neural network