Lung-GANs: Unsupervised Representation Learning for Lung Disease Classification Using Chest CT and X-Ray Images

Lung diseases are a tremendous challenge to the health and life of people globally, accounting for 5 out of 30 most common causes of death. Early diagnosis is crucial to help in faster recovery and improve long-term survival rates. Deep learning techniques offer a great promise for automated, fast, and reliable detection of lung diseases from medical images. Specifically, convolutional neural networks have accomplished encouraging results in disease detection. In spite of that, the performance of such supervised models depends heavily on the availability of large labeled data, the collection of which is an expensive and tedious task, specially for a novel disease. Therefore, in this article, we propose a deep unsupervised framework to classify lung diseases from chest CT and X-ray images. Our framework introduces multiple-layer generative adversarial networks called Lung-GANs that learn interpretable representations of lung disease images using only unlabeled data. We use the lung features learned by the model to train a support vector machine and a stacking classifier. We demonstrate through experiments that the proposed method outperforms the current state-of-the-art unsupervised models in lung disease classification. Our model obtained an accuracy of 94%-99.5% on all the six large-scale publicly available lung disease datasets used in this study. Hence, the proposed framework will simplify lung disease detection by reducing the time for diagnosis and increasing the convenience of diagnostics.