CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

Fatima Zahra El Biach,Hicham Laanaya,Imad Iala,Khalid Minaoui

doi:10.14569/ijacsa.2022.0130162

Fatima Zahra El Biach, Hicham Laanaya + Show 2 more

Open Access

https://doi.org/10.14569/ijacsa.2022.0130162

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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.

Highlights

In December 2019, a viral pneumonia epidemic of unknown etiology emerged in Wuhan city, Hubei province, China [1]
In order to detect COVID-19 lung infections using CT images, we propose the CovSeg-UNet approach, which characterized by an end-to-end architecture based on one of the most robust approaches in biomedical image segmentation that is U-Net
To cope with this task, we have proposed an end-to-end architecture similar to U-Net, the proposed method network learns the discriminating features of lung infections from CT images to establish an image-to-image mapping relationship

Summary

Introduction

In December 2019, a viral pneumonia epidemic of unknown etiology emerged in Wuhan city, Hubei province, China [1]. On January 9, 2020, the World Health Organization (WHO) and Chinese Health Authorities officially announced the discovery of a new coronavirus. This pneumonia is an infectious disease caused by a virus identified under the name SARS-CoV-2 (Severe Acute Respiratory Syndrome CoronaVirus-2) by the ICTV (International Committee on Taxonomy of Viruses) [2], and causing a disease called COVID-19 (COronaVIrus Disease 2019). The reservoir of this virus is probably animal. SARS-CoV-2 closely resembles a virus detected in a bat, the animal that transmits it to humans has yet to be identified with certainty. Several research studies suggest that the pangolin, a small mammal eaten in southern China, could be involved as an intermediate host between bats and humans

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