Keratoconus is a progressive corneal disease that mostly occurs in adolescence and can cause ir-regular astigmatism and vision loss.Late-stage blindness requires corneal transplantation.Therefore,ear-ly and accurate screening of keratoconus is necessary to prevent the progression of the disease and avoid deterioration.As a classic algorithm,neural network is an effective tool for keratoconus diagnosis.How-ever,as the data of keratoconus cases grows day by day,in order to make full use of the new data,it is often necessary to retrain all samples,which will consume a lot of time.In order to solve the above prob-lems,this article proposes an incremental learning algorithm integrating neural networks to achieve intel-ligent diagnosis of keratoconus.In addition,this article also introduces the ideas of undersampling and cost sensitivity to solve the problem that existing incremental learning algorithms cannot handle imbal-anced data.Experimental results show that the recognition accuracy of the algorithm proposed in this ar-ticle reaches 97％,and requires short training time and less storage space.Therefore,this algorithm can assist in the diagnosis of keratoconus more efficiently.
关键词
圆锥角膜/集成神经网络/增量学习/不平衡数据
Key words
keratoconus/integrated neural network/incremental learning/unbalanced data
维普
万方数据