Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network.

• A computer-aided diagnosis (CAD) system was proposed to diagnose the lung cancer in endobronchial ultrasound (EBUS) images. • In this study, a CAD system using the convolutional neural network (CNN) was proposed to automatically differentiate benign and malignant lesions for early detecting lung cancer. • The results of our CAD system show that the accuracy, sensitivity, specificity, and the area under ROC achieved 85.4%, 87.0%, 82.1%, and 0.8705, respectively. • Our study has demonstrated that CAD system based on the CNN architecture could provide an automatic method to differentiate benign and malignant lesions in patients with lung cancer. In the United States, lung cancer is the leading cause of cancer death. The survival rate could increase by early detection. In recent years, the endobronchial ultrasonography (EBUS) images have been utilized to differentiate between benign and malignant lesions and guide transbronchial needle aspiration because it is real-time, radiation-free and has better performance. However, the diagnosis depends on the subjective judgment from doctors. In some previous studies, which using the grayscale image textures of the EBUS images to classify the lung lesions but it belonged to semi-automated system which still need the experts to select a part of the lesion first. Therefore, the main purpose of this study was to achieve full automation assistance by using convolution neural network. First of all, the EBUS images resized to the input size of convolution neural network (CNN). And then, the training data were rotated and flipped. The parameters of the model trained with ImageNet previously were transferred to the CaffeNet used to classify the lung lesions. And then, the parameter of the CaffeNet was optimized by the EBUS training data. The features with 4096 dimension were extracted from the 7th fully connected layer and the support vector machine (SVM) was utilized to differentiate benign and malignant. This study was validated with 164 cases including 56 benign and 108 malignant. According to the experiment results, applying the classification by the features from the CNN with transfer learning had better performance than the conventional method with gray level co-occurrence matrix (GLCM) features. The accuracy, sensitivity, specificity, and the area under ROC achieved 85.4% (140/164), 87.0% (94/108), 82.1% (46/56), and 0.8705, respectively. From the experiment results, it has potential ability to diagnose EBUS images with CNN. [ABSTRACT FROM AUTHOR]