TY - GEN
T1 - Integrating YOLO and 3D U-Net for COVID-19 Diagnosis on Chest CT Scans
AU - Valverde-Rebaza, Jorge
AU - Andreis, Guilherme R.
AU - Shiguihara, Pedro
AU - Paucar, Sebastian
AU - Mano, Leandro Y.
AU - Goes, Fabiana
AU - Noguez, Julieta
AU - Da Silva, Nathalia C.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - COVID-19
KW - Deep Learning
KW - Lung injury detection
KW - Medical image segmentation and classification
KW - Transfer Learning
UR - https://www.scopus.com/pages/publications/85200477238
U2 - 10.1109/CBMS61543.2024.00011
DO - 10.1109/CBMS61543.2024.00011
M3 - Contribución a la conferencia
AN - SCOPUS:85200477238
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 15
EP - 20
BT - Proceedings - 2024 IEEE 37th International Symposium on Computer-Based Medical Systems, CBMS 2024
A2 - Ochoa-Ruiz, Gilberto
A2 - Grisan, Enrico
A2 - Ali, Sharib
A2 - Sicilia, Rosa
A2 - Santamaria, Lucia Prieto
A2 - Kane, Bridget
A2 - Daul, Christian
A2 - Ante, Gildardo Sanchez
A2 - Gonzalez, Alejandro Rodriguez
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2024
Y2 - 26 June 2024 through 28 June 2024
ER -
