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

• 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
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2017.7966231

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.