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Uncertainty Propagation and Salient Features Maps in Deep Learning Architectures for Supporting Covid-19 Diagnosis

Publicat a:Studies In Computational Intelligence. 1023 1-22 - 2022-01-01 1023(), DOI: 10.1007/978-3-031-04597-4_1

Autors: Rodríguez-García I; Sáchez-Pastor T; Vázquez-Escobar J; Gómez-González JL; Cárdenas-Montes M

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Doubt and the ability to point out details that identify an object or categories of objects are peculiarities of human intelligence. Roughly speaking, artificial intelligence aims to mimic the behavior of human intelligence. This work is a first attempt at joint use of previously existing technologies to mimic these characteristics of human intelligence. This work aims to help in the diagnosis of X-ray chest images with pneumonia, and covid-19; and images of healthy individuals by applying deep neural networks. These deep neural networks are modified so that they can generate predictions with uncertainty. Subsequently, on the previously generated predictions, the salient feature maps are generated to identify on which parts of the image the forecast decision is based. As a result of the work, examples of X-ray chest images will be shown where independent executions predict different labels, focusing attention on different areas of the radiography. So that the different areas indicated by the independent runs might help in the diagnosis of the different pathologies. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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