# Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease

## TL;DR

This paper presents a deep learning method that accurately differentiates between Celiac Disease, Environmental Enteropathy, and normal tissue in duodenal biopsies, aiding diagnosis with over 90% accuracy and interpretability tools.

## Contribution

It introduces a novel deep learning approach for classifying gastrointestinal conditions in histopathological images, with emphasis on interpretability.

## Key findings

- Achieved over 90% accuracy in distinguishing conditions
- Utilized Gradient-weighted Class Activation Mappings for model interpretation
- Demonstrated the effectiveness of deep learning in histopathology diagnosis

## Abstract

Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.