Deep Metric Learning for Transparent Classification of Covid-19 X-Ray Images
16th IEEE International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2022
; : 300-307, 2022.
Article
in English
| Scopus | ID: covidwho-2313329
ABSTRACT
This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A K-Nearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to the classification, a characteristic which can be very useful in the medical domain, e.g. in a decision support system. © 2022 IEEE.
Chest-X-ray; Covid-19; embeddings; image diagnosis; Classification (of information); Computer aided diagnosis; Decision support systems; Deep learning; Image classification; Image enhancement; Nearest neighbor search; Chest X-ray image; Image embedding; Learning models; Metric learning; Nonlinear projections; X-ray image
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
16th IEEE International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2022
Year:
2022
Document Type:
Article
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