In vivo classification of inflammation in blood vessels with convolutional neural networks

An emerging field in medical diagnostics is the study of micro-circulations in blood vessels. Several characteristics of the micro-circulations in blood vessels have been shown to predict inflammation in a patient's tissue. The characteristics are video recorded via a camera inserted into the s...

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Bibliographic Details
Main Authors: McIlroy, Stuart, Kubo, Yoshimasa, Trappenberg, Thomas, Toguri, James, Lehmann, Christian
Format: Conference Proceeding
Language:English
Subjects:
Animals
Biomedical imaging
Blood
Cameras
Neural networks
Training
Online Access:Request full text
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Summary:An emerging field in medical diagnostics is the study of micro-circulations in blood vessels. Several characteristics of the micro-circulations in blood vessels have been shown to predict inflammation in a patient's tissue. The characteristics are video recorded via a camera inserted into the subject. At present, the analysis of the videos are done manually by visual inspection to determine inflammation. In our paper, we propose a technique to automatically classify the videos as containing inflammation or not. Our technique uses a convolutional neural network which classifies many different segments of images from a video and averages the predictions. Our network achieves an accuracy of 83%. We further divide inflammation into extreme and moderate inflammation and our network achieves an accuracy of 80%. This is the first step in developing methods that can perform a better quantitative analysis of inflammation to speed up medical diagnosis.
ISSN:2161-4407
DOI:10.1109/IJCNN.2017.7966231