Tissue classification on histological images is a useful alternative to manual histology analysis, and has been well-studied in a variety of machine learning approaches. However, classification of whole slide images at high resolution is a difficult and computationally-intensive task. In addition, many tissue analysis tasks are targeted at identifying rare or small regions of tissue. In colon cancer, small groups of tumor cells (tumor buds) exist on the front edge of the invasive tumor region and are an important indicator of cancer aggressiveness. These small objects are difficult or impossible to detect when examining an image at lower resolution, while running the classifier at an appropriate high resolution can be time consuming. In this work, a two-tier convolutional neural network classification approach is explored to identify small but important tissue regions on whole-slide tissue scans. The first tier is a coarse-level classifier trained with patches extracted from the image at a low power field (4x optical magnification), designed to identify two main tissue types: tumor and nontumor areas. Regions that are likely to contain tumor buds (non-tumor regions) are passed to a fine-level classifier that classifies the patches into 9 additional tissue types at a high-power field (40x). The system achieves a 43% reduction in processing time (3 hours to 1.7 hours for a 19,200-by-19,200 pixel image). The two-tier classifier provides an efficient whole-slide tissue classification by narrowing down the regions of interest, increasing the chances of tumor buds being identified.