A new approach for detecting eosinophils in the gastrointestinal tract and diagnosing eosinophilic colitis

Abstract EN

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%.