Integrating YOLO and 3D U-Net for COVID-19 Diagnosis on Chest CT Scans

Jorge Valverde-Rebaza*, Guilherme R. Andreis, Pedro Shiguihara, Sebastian Paucar, Leandro Y. Mano, Fabiana Goes, Julieta Noguez, Nathalia C. Da Silva

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The Coronavirus disease 2019 (COVID-19) pandemic has presented unprecedented challenges to global health-care systems, urgently calling for innovative diagnostic solutions. This paper introduces the Fully Automatic Detection of Covid-19 cases in medical Images of the Lung (FADCIL) system, a cutting-edge deep learning framework designed for rapid and accurate COVID-19 diagnosis from chest computed tomography (CT) images. By leveraging an architecture based on YOLO and 3D U-Net, FADCIL excels in identifying and quantifying lung injuries attributable to COVID-19, distinguishing them from other pathologies. In real-world clinical environments, FADCIL achieves a DICE coefficient above 0.82, highlighting its robust performance and clinical relevance. FADCIL also enhances the reliability of COVID-19 assessment, empowering healthcare professionals to make informed decisions and effectively manage patient care. Thus, this paper outlines the FADCIL architecture and presents an in-depth analysis of quantitative and qualitative evaluation results derived from a novel dataset comprising over 1000 CT scans. Furthermore, we provide access to the FADCIL's source code for public use.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2024 IEEE 37th International Symposium on Computer-Based Medical Systems, CBMS 2024
EditoresGilberto Ochoa-Ruiz, Enrico Grisan, Sharib Ali, Rosa Sicilia, Lucia Prieto Santamaria, Bridget Kane, Christian Daul, Gildardo Sanchez Ante, Alejandro Rodriguez Gonzalez
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas15-20
Número de páginas6
ISBN (versión digital)9798350384727
DOI
EstadoPublicada - 2024
Evento37th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2024 - Hybrid, Guadalajara, México
Duración: 26 jun. 202428 jun. 2024

Serie de la publicación

NombreProceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (versión impresa)1063-7125

Conferencia

Conferencia37th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2024
País/TerritorioMéxico
CiudadHybrid, Guadalajara
Período26/06/2428/06/24

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