Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (3): 259-267.doi: 10.11959/j.issn.2096-6652.202127

• Special Issue: Intelligent Object Detection and Recognition • Previous Articles     Next Articles

Retinal multi-disease screening and recognition method based on deep convolution ensemble network

Heyang WANG, Qiming YANG, Qi ZHU   

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Revised:2021-07-08 Online:2021-09-15 Published:2021-09-01
  • Supported by:
    Fundamental Research Funds for the Central Universities(NT2020024)

Abstract:

As for the characteristics of various types of retinal diseases and uncertainty of the location of the lesions, a retinal multi-disease screening and recognition method based on deep convolutional ensemble network was proposed.Firstly, the black borders on both sides of the retinal fundus image were cut off, and the noise in the image was removed to reduce the interference to the retinal image and increase the clarity of the image.After that, data augmentation methods such as cropping and rotating were performed to process retinal fundus image to amplify the dataset.Then, a model based on deep convolutional neural network was built for feature extraction, and the network model was fine-tuned to complete the task of screening and identifying retinal diseases.Finally, the results of multiple models were ensembled.The experimental results show that this method has achieved good results for the screening and recognition of retinal diseases, the accuracy of retinal disease screening is 96.05%, and the accuracy of retinal disease recognition is 72.55%.

Key words: retinal fundus image, disease screening, disease recognition, deep convolutional network, ensemble model

CLC Number: 

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