Features Of ICU Admission In X-Ray Images Of Covid-19 Patients

2021

Gomes, Douglas P S

Ulhaq, Anwaar

Paul, Manoranjan

Horry, Michael J

Chakraborty, Subrata

Saha, Manash

Debnath, Tanmoy

Motiur Rahaman, D M

Conference Publication

en

Publication

Institute of Electrical and Electronics Engineers (IEEE)

Piscataway, United States of America

Proceedings of the International Conference on Image Processing

10.1109/ICIP42928.2021.9506266

une:1959.11/44124

This paper presents an original methodology for extracting semantic features from X-rays images that correlate to severity from a data set with patient ICU admission labels through interpretable models. The validation is partially performed by a proposed method that correlates the extracted features with a separate larger data set that does not contain the ICU-outcome labels. The analysis points out that a few features explain most of the variance between patients admitted in ICUs or not. The methods herein can be viewed as a statistical approach highlighting the importance of features related to ICU admission that may have been only qualitatively reported. In between features shown to be over-represented in the external data set were ones like 'Consolidation' (1.67), 'Alveolar' (1.33), and 'Effusion' (1.3). A brief analysis on the locations also showed higher frequency in labels like 'Bilateral' (1.58) and Peripheral (1.28) in patients labelled with higher chances to be admitted in ICU. To properly handle the limited data sets, a state-of-the-art lung segmentation network was also trained and presented, together with the use of low-complexity and interpretable models to avoid overfitting.

link

2021 IEEE International Conference on Image Processing (ICIP), p. 200-204

9781665441155

9781665431026

200

204