A deep learning approach for COVID-19 screening and localization on chest x-ray images

Karem Daiane Marcomini; Diego Armando Cardona Cardenas; Agma Juci Machado Traina; José Eduardo Krieger; Marco Antonio Gutierrez

doi:10.1117/12.2613177

4 April 2022 A deep learning approach for COVID-19 screening and localization on chest x-ray images

Karem Daiane Marcomini, Diego Armando Cardona Cardenas, Agma Juci Machado Traina, José Eduardo Krieger, Marco Antonio Gutierrez

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203327 (2022) https://doi.org/10.1117/12.2613177
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Chest X-ray (CXR) images have a high potential in the monitoring and examination of various lung diseases, including COVID-19. However, the screening of a large number of patients with diagnostic hypothesis for COVID-19 poses a major challenge for physicians. In this paper, we propose a deep learning-based approach that can simultaneously suggest a diagnose and localize lung opacity areas in CXR images. We used a public dataset containing 5, 639 posteroanterior CXR images. Due to unbalanced classes (69.2% of the images are COVID-19 positive), data augmentation was applied only to images belonging to the normal category. We split the dataset into train and test sets with proportional rate at 90:10. To the classification task, we applied 5-fold cross-validation to the training set. The EfficientNetB4 architecture was used to perform this classification. We used a YOLOv5 pre-trained in COCO dataset to the detection task. Evaluations were based on accuracy and area under the ROC curve (AUROC) metrics to the classification task and mean average precision (mAP) to the detection task. The classification task achieved an average accuracy of 0.83 ± 0.01 (95% CI [0.81, 0.84]) and AUC of 0.88 ± 0.02 (95% CI [0.85, 0.89]) in 5-fold over the test dataset. The best result was reached in fold 3 (0.84 and 0.89 of accuracy and AUC, respectively). Positive results were evaluated by the opacity detector, which achieved a mAP of 59.51%. Thus, the good performance and rapid diagnostic prediction make the system a promising means to assist radiologists in decision making tasks.

Citation Download Citation

Karem Daiane Marcomini, Diego Armando Cardona Cardenas, Agma Juci Machado Traina, José Eduardo Krieger, and Marco Antonio Gutierrez "A deep learning approach for COVID-19 screening and localization on chest x-ray images", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203327 (4 April 2022); https://doi.org/10.1117/12.2613177

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available