By using Machine Learning (ML) techniques it's possible to acknowledge patterns in digital images and to classify these images supported their contents with high accuracy. Pattern recognition is one among the most parameters employed by pathologists within the analysis of biopsy material. We therefore focused our study on pattern recognition and not on object (cell) detection/ classification. The aim of our study is to create and train one algorithm which can pre-analyse digital images of various sorts of intestinal mucosa. Digital images from gastric (51) and colon (92) mucosal biopsies were labeled normal or abnormal. Images of gastric biopsies were labeled as abnormal when following histological features were present: increased number of inflammatory cells, interstitial oedema and differentiation abnormalities of the epithelial lining. Images of colon biopsies were labeled as abnormal when distortion of the glands, villous structures, differentiation abnormalities of the epithelial lining, increased number of inflammatory cells were found. All images showing no abnormalities were labeled as normal. With these data sets we trained different machine learning algorithms to classify the digital images. the simplest performing algorithm, a support vector machine classifier, achieved an accuracy of 94% on colon images and 75% on gastric images. This pilot study illustrates the likelihood to coach an algorithm on a limited data set in order that it classifies with acceptable accuracy.
