Abstract: COVID-19 epidemic continues to threaten public health with the appearance of new, more severe mutations, and given the delay in the vaccination process, the situation becomes more complex. Thus, the implementation of rapid solutions for the early detection of this virus is an immediate priority. To this end, we provide a deep learning method called CovSeg-Unet to diagnose COVID-19 from chest CT images. The CovSeg-Unet method consists in the first time of preprocessing the CT images to eliminate the noise and make all images in the same standard. Then, CovSeg-Unet uses an end-to-end architecture to form the network. Since CT images are not balanced, we propose a loss function to balance the pixel distribution of infected/uninfected regions. CovSeg-Unet achieved high performances in localizing COVID-19 lung infections compared to others methods. We performed qualitative and quantitative assessments on two public datasets (Dataset-1 and Dataset-2) annotated by expert radiologists. The experimental results prove that our method is a real solution that can better help in the COVID-19 diagnosis process.

Fatima Zahra EL BIACH, Imad IALA, Hicham LAANAYA and Khalid MINAOUI, “CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130162

@article{BIACH2022,
title = {CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130162},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130162},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Fatima Zahra EL BIACH and Imad IALA and Hicham LAANAYA and Khalid MINAOUI}
}