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The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images
The lack, due to privacy concerns, of large public databases of medical
pathologies is a well-known and major problem, substantially hindering the
application of deep learning techniques in this field. In this article, we
investigate the possibility to supply to the deficiency in the number of data
by means of data augmentation techniques, working on the recent Kvasir dataset
of endoscopical images of gastrointestinal diseases. The dataset comprises
4,000 colored images labeled and verified by medical endoscopists, covering a
few common pathologies at different anatomical landmarks: Z-line, pylorus and
cecum. We show how the application of data augmentation techniques allows to
achieve sensible improvements of the classification with respect to previous
approaches, both in terms of precision and recall
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