Automatic diagnosis of gastropathy disease plays an important role in supporting the clinician and speeding up the examination process. In this paper, we propose a general algorithm for stomach disease detection using sparse representation, which can get robust result even using a small size of high-dimensional training dataset. First, a noval large scale dictionary selection model based on the low rank property is designed to initialize the meaningful dictionary with minimal size. Then in comparison with the state-of-the-arts, we consider disease diagnosing as both supervised classification issue and one-class classification issue, where a new criterion, weighted sparsity concentration index (WSCI) is designed for outlier detection. Experiments on our dataset and comparisons with state-of-the-arts demonstrate the effectiveness and efficiency of our algorithm.