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Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks

  • Kamran Kowsari
  • Rasoul Sali
  • Marium N. Khan
  • William Adorno
  • S. Asad Ali
  • Sean R. Moore
  • Beatrice C. Amadi
  • Paul Kelly
  • Sana SyedEmail author
  • Donald E. BrownEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)

Abstract

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.

Keywords

Convolutional neural networks Medical imaging Celiac Disease Environmental Enteropathy 

Notes

Acknowledgments

This research was supported by University of Virginia, Engineering in Medicine SEED Grant \( (SS~ \& ~DEB)\), the University of Virginia Translational Health Research Institute of Virginia (THRIV) Mentored Career Development Award (SS), and the Bill and Melinda Gates Foundation  (AA,  OPP1138727; SRM, OPP1144149; PK,  OPP1066118)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kamran Kowsari
    • 1
  • Rasoul Sali
    • 1
  • Marium N. Khan
    • 3
  • William Adorno
    • 1
  • S. Asad Ali
    • 4
  • Sean R. Moore
    • 3
  • Beatrice C. Amadi
    • 5
  • Paul Kelly
    • 5
    • 6
  • Sana Syed
    • 2
    • 4
    Email author
  • Donald E. Brown
    • 1
    • 2
    Email author
  1. 1.Department of Systems and Information EngineeringUniversity of VirginiaCharlottesvilleUSA
  2. 2.School of Data ScienceUniversity of VirginiaCharlottesvilleUSA
  3. 3.Department of PediatricsUniversity of VirginiaCharlottesvilleUSA
  4. 4.Department of Pediatrics and Child HealthAga Khan UniversityKarachiPakistan
  5. 5.Tropical Gastroenterology and Nutrition GroupUniversity of Zambia School of MedicineLusakaZambia
  6. 6.Blizard Institute, Barts and The London School of MedicineQueen Mary University of LondonLondonUK

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