Improved CNN Multi-classification of Retinal Diseases Based on SENet and GBDT

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    Low efficiency, missed diagnosis and misdiagnosis exist in the manual diagnosis and classification of fundus retinal images. To this end, a convolutional network model based on the attention mechanism SENet and GBDT gradient boosting classification method is proposed to help physicians distinguish the fundus screening results of various diseases and reduce the rate of missed and false detection. Based on the deep learning model, the sampling convolutional network is applied to learn the extracted three characteristics of retinal hemorrhage, optic disc edema and macular degeneration, and the GBDT gradient boosting method is employed for identification and classification. The real clinical data provided by the Third People’s Hospital of Dalian are used to evaluate the performance of the proposed method. The results show that the average accuracy, precision, and recall rates of the model reach 99.27%, 98.35%, and 0.9810 respectively, and the model has certain practical value in the clinical diagnosis of retinal diseases.

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  • Received:January 29,2023
  • Revised:February 23,2023
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  • Online: May 22,2023
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