A New Approach for Detecting Eosinophils in the
Gastrointestinal Tract and Diagnosing Eosinophilic
Colitis
Amal Alzu’bi1, Hassan Najadat1,
Walaa Eyadat2, Alia Al-Mohtaseb3, and Hussam Haddad4
1Department
of Computer Information Systems, Jordan University of Science and Technology,
Jordan 2Department of Computer Science, Jordan University of Science
and Technology, Jordan
3Department
of Pathology and Microbiology, Jordan University of Science and Technology/
King Abdullah University Hospital, Jordan
4Department
of Pathology and Microbiology, King Abdullah University Hospital, Jordan
Abstract: Eosinophilic Gastrointestinal
Diseases (EGIDs) represent a rare group of disorders that can have various
clinical presentations dependent on the involved segment within the
gastrointestinal tract. Eosinophilic Colitis is considered as an
under-diagnosed disease, which requires more attention and correct diagnosis.
Our research aims to develop an image processing and machine learning approach
that can be utilized by pathologists to diagnose patients with Eosinophilic
Colitis in an easy and fast manner. The approach tends to enable pathologists
to detect eosinophils in the microscopic sections of the gastrointestinal tract
including; the esophagus and colon. We proposed an approach that relies on
applying advanced image processing techniques on the digitally acquired images
of microscopic biopsies to extract the primary features of the eosinophils and to
estimate the count of the eosinophils in the given patient’s slide. These
counts were used as inputs to machine learning algorithms including, Support
Vector Machine (SVM) and Neural Networks in order to decide whether the patient
has eosinophilic colitis disease or not. The accuracy of detecting Eosinophilic
Colitis using SVM classifier is 85.71%, and in neural network is 93.8%.
Keywords: Eosinophilic colitis,
eosinophils, eosinophilic gastroenteritis, image processing, digital images,
neural network, SVM.
Received July 13, 2020; accepted December 10, 2020