A Deep Learning-Based Pipeline for Celiac Disease Diagnosis Using Histopathological Images

Farhad Maleki, Kevin Cote, Keyhan Najafian, Katie L. Ovens, Yan Miao, Rita Zakarian, Caroline Reinhold, Reza Forghani, Peter Savadjiev, Zu-hua Gao

Abstract: With an increasing number of celiac disease diagnoses and the increasing number of misdiagnoses, automated approaches are valuable to aid pathologists in efficiently diagnosing this disease. Histopathological analysis of intestinal biopsy is considered the gold standard for diagnosis. Convolutional neural networks have achieved promising results for various image processing tasks. A common challenge in medical imaging analysis is obtaining a large number of samples, impeding the full potential of deep learning. In this paper, we propose a classification pipeline to train deep convolutional neural networks to accurately diagnosis celiac disease using models trained with a small number of samples. To show the utility of this approach, we compared it to a typical classification pipeline. The results indicate the superiority of our classification pipeline in distinguishing celiac disease from normal tissue with precision, recall, and accuracy of 0.941, 0.889, and 0.893, respectively. Although we showed the utility of the proposed pipeline for celiac diagnosis, it can also be used for other applications utilizing histopathological imaging.