Diagnosis of secondary pulmonary tuberculosis by an eight-layer improved convolutional neural network with stochastic pooling and hyperparameter optimization

Abstract

To more efficiently diagnose secondary pulmonary tuberculosis, we build an improved convolutional neural network (ICNN) based on recent deep learning technologies. First, a 12-way data augmentation (DA-12) was proposed to increase size of training set. Second, stochastic pooling was introduced to replace the standard average pooling and max pooling. Third, batch normalization and dropout techniques were included and associated with conv layers and fully-connected layers, respectively. Fourth, a dynamic learning rate was employed to replace traditional fixed learning rate. Fifth, hyperparameter optimization was used to optimize the number of layers within proposed network. Our eight-layer ICNN demonstrated excellent results on the test set, yielding a sensitivity of 94.19%, a specificity of 93.72%, and an accuracy of 93.95%. Our ICNN provides better performances than other four state-of-the-art algorithms. It can help radiologists to make more accurate diagnosis on secondary pulmonary tuberculosis.