title: Thorax disease diagnosis using deep convolutional neural network.
abstract: Computer aided diagnosis (CAD) is an important issue, which can significantly improve the efficiency of doctors. In this paper, we propose a deep convolutional neural network (CNN) based method for thorax disease diagnosis. We firstly align the images by matching the interest points between the images, and then enlarge the dataset by using Gaussian scale space theory. After that we use the enlarged dataset to train a deep CNN model and apply the obtained model for the diagnosis of new test data. Our experimental results show our method achieves very promising results.
authors.lastname: Jie Chen
authors.lastname: Xianbiao Qi
authors.forename: Osmo
authors.initials: O
authors.lastname: Tervonen
authors.forename: Olli
authors.initials: O
authors.lastname: Silven
authors.lastname: Guoying Zhao
authors.forename: Matti
authors.initials: M
authors.lastname: Pietikainen
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D013896:Thoracic Diseases / Q000175:diagnosis*