A classification algorithm for ultrasound images in ophthalmology
In order to improve the classification effect of convolutional neural network used in ophthalmic ultrasound image datas-ets,there are problems such as low image contrast interference,wrong classification and omission of classification in the current oph-thalmic ultrasound datasets.Based on the GoogleNet classification network model,an improved ophthalmic ultrasound image classifica-tion method is designed,which introduces recursive gated convolution and a multi-scale structure composed of convolution groups of different sizes to enhance the feature extraction ability of the network model.Extensive experiments are performed on ophthalmic ultra-sound datasets.The experimental results show that compared with the original GoogleNet network model,the accuracy rate,macro pre-cision rate,macro recall rate and macro F1 score have increased by 1.41%,4.36%,5.02%,and 5.14%,respectively.Compared with mainstream classification networks such as EfficientNetB0 and ResNet50,the accuracy rates have increased by 2.21%and 5.03%,re-spectively.
万方数据
维普
