| dc.description.abstract | detecting diseases in high resolution endoscopy videos can be done in several ways
depending on the methodology for detection. one such method that has been a hot topic
in the field of medical technology research is the implementation of machine learning
techniques to aid in the diagnosis of networks. while, this has been studied extensively
with traditional machine learning methods and more recently neural networks, major
issues persist in their implementation in everyday health. among the largest issues is the
size of the training data needed to make accurate prediction, as well as the inability to
generalize the networks to several disease. we address these issues with a novel
approach to detecting inflammatory bowel diseases, specifically crohn’s disease in
endoscopy videos. we use thick data analytics to teach a network to detect heuristics of
a disease, not to simply make classifications from images. using heuristic annotations
like bounding boxes and segmentation masks, we train a siamese neural network to
detect video frames for ulcers, polyps, erosions, and erythema with accuracies as high
as 87.5% for polyps and 77.5% for ulcers. we then implement this network in a protype
frontend that physicians can use to upload videos and receive the processed images in
an interactive format. we also pontificate as to how our approach and prototype can be
expanded to several diseases with learning of more heuristics. | en_us |
