Medical Image Text Area Detection Based on Feature Reuse Convolutional Neural Network

In order to solve the problem of Chinese medical image text being missed and misdetected under the CTPN model, a new convolutional neural network DVNet based on the fusion of VGG convolutional neural network and DenseNet dense network was proposed. DVNet takes the first two layers of VGG network for deep feature extraction, and then connects DenseNet dense modules. Using the idea of feature reuse, the features of the front convolutional layer and the features of the back convolutional layer are output together. During post-processing, NMS is used to filter out redundant text boxes. In the Chinese medical text data set provided, three different networks, VGG, DenseNet and DVNet, were used to detect the text. The experimental results showed that the precision rate of DVNet were improved by 2%-3% compared with VGG and DenseNet.