  Normally eosinophils are present in the gastrointestinal (GI) tract of
healthy individuals. When the eosinophils increase beyond their usual amount in
the GI tract, a patient gets varied symptoms. Clinicians find it difficult to
diagnose this condition called eosinophilia. Early diagnosis can help in
treating patients. Histopathology is the gold standard in the diagnosis for
this condition. As this is an under-diagnosed condition, counting eosinophils
in the GI tract biopsies is important. In this study, we trained and tested a
deep neural network based on UNet to detect and count eosinophils in GI tract
biopsies. We used connected component analysis to extract the eosinophils. We
studied correlation of eosinophilic infiltration counted by AI with a manual
count. GI tract biopsy slides were stained with H&E stain. Slides were scanned
using a camera attached to a microscope and five high-power field images were
taken per slide. Pearson correlation coefficient was 85% between the
machine-detected and manual eosinophil counts on 300 held-out (test) images.
