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%.

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[22] Zhang Y., Li Y., and Su J., “Iterative Learning Control for Image Feature Extraction with Multiple- Image Blends,” EURASIP Journal on Image and Video Processing, vol. 2018, no. 1, pp. 100, 2018. A New Approach for Detecting Eosinophils in the Gastrointestinal Tract… 603 Amal Alzu’bi joined Jordan University of Science and Technology (JUST) as an assistant professor of Computer Information Systems in 2017. She earned a Ph.D in Health Information Management from University of Pittsburgh, PA, USA in 2016. Her research interests include, health informatics, health data mining and analysis, and genomics. Hassan Najadat joined Jordan University of Science and Technology (JUST) as an assistant professor of Computer Science in 2005 and became an associate professor in 2011. His research interests are in artificial intelligence and data science including data engineering, data mining approaches, machine learnings, and data analysis with emphasis on applying different analyzing approaches in health sector, information systems, and data envelopment analysis. Walaa Eyadat graduated from the faculty of Computer Science, Al- Balqa' Applied University, Jordan in 2015. She received her Master degree in 2020 in Computer Science from the Faculty of Computer Science, Jordan University of Science and Technology, Irbid, Jordan. Her main research interests are in the areas of data mining and machine learning. Alia Al-Mohtaseb graduated from medical school at Jordan University of Science and Technology, completed her postgraduate studies in pathology and the laboratory medicine from the same university. Further training and fellowship in the royal infirmary of Edinburgh, United Kingdom. Currently working as an assistant professor at Jordan University of Science and Technology and as a consultant at King Abdullah University Hospital. Her research interests include gynecological and breast pathology, and digital pathology. Hussam Haddad graduated with a bachelor of Medicine and Surgery from Jordan University of Science and Technology in 2012. He completed his residency in Pathology from King Abdullah University Hospital and obtained the Jordanian board of Pathology in 2020. Currently, he is a pathology specialist at AlBasheer Hospital.