The diagnosis of Crohn's disease (CD) can be challenging given variation in anatomic disease distribution, morphology, and proportion of intestine affected. Subsequently, the appearance and presentation of disease on cross-sectional imaging are a heterogeneous combination of shapes and image features, making differentiation of normal vs. diseased small intestine prone to inter-observer variation. Applying machine learning methods to cross-sectional, imaging interpretation may improve the accuracy of CD diagnosis and distinguish normal from diseased intestine by automated approaches. Using a set of 207 CT-enterography (CTE) scans, two independent radiologists labeled the presence of disease vs. non-disease at 7.5mm intervals along the length of the bowel (mini-segments), generating a dataset of 10,552 observations for model training and testing. We introduce two types of classifiers to quantitatively assess CD related intestinal damage for each mini-segment. The sensitivity, specificity and AUC for the best performing ensemble and CNN models are 84.9%, 84.7%, 0.93, and 90.9%, 78.6%, 0.92 respectively. The accuracy for classifying full segments as diseased vs. normal using ensemble and CNN models are 96.3% and 90.7% respectively.