Résumé
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.
Langue d'origine | English |
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Titre de la publication principale | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings |
Maison d'édition | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3022-3027 |
Nombre de pages | 6 |
ISBN (électronique) | 9781509061815 |
DOI | |
Statut de publication | Published - juin 30 2017 |
Événement | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Durée: mai 14 2017 → mai 19 2017 |
Séries de publication
Prénom | Proceedings of the International Joint Conference on Neural Networks |
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Volume | 2017-May |
Conference
Conference | 2017 International Joint Conference on Neural Networks, IJCNN 2017 |
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Pays/Territoire | United States |
Ville | Anchorage |
Période | 5/14/17 → 5/19/17 |
Note bibliographique
Publisher Copyright:© 2017 IEEE.
ASJC Scopus Subject Areas
- Software
- Artificial Intelligence