Capsule endoscopy (CE) provides noninvasive access to a large part of the small bowel that is otherwise inaccessible without invasive and traumatic treatment. However, it also produces large amounts of data (approximately 50 000 images) that must be then manually reviewed by a clinician. Such large datasets provide an opportunity for application of image analysis and supervised learning methods. Automated analysis of CE images has only focused on detection, and often only for bleeding. Compared to these detection approaches, we explored assessment of discrete disease for lesions created by mucosal inflammation in Crohn's disease (CD). Our work is the first study to systematically explore supervised classification for CD lesions, a classifier cascade to classify discrete lesions, as well as quantitative assessment of lesion severity. We used a well-developed database of 47 studies for evaluation of these methods. The developed methods show high agreement with ground truth severity ratings manually assigned by an expert, and good precision (>;90% for lesion detection) and recall (>;90%) for lesions of varying severity.