COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

Amara, Kahina; Aouf, Ali; Kennouche, Hoceine; Djekoune, Oualid; Zenati, Nadia; Kerdjidj, Oussama; Ferguene, Farid

doi:10.1016/j.cag.2022.03.003

Cited by 14 publications

(2 citation statements)

References 23 publications

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“…This section entails providing an in-depth explanation of the state-of-the-art research utilizing CT scan images and deep learning for COVID detection. At a broader level, these research studies can be classified into two types (a) COVID Detection ( Bougourzi, Contino et al, 2021 , Lassau et al, 2021 , Li et al, 2021 , Song et al, 2021 , Ye et al, 2022 ) (b) COVID Segmentation ( Amara et al, 2022 , Bose et al, 2022 , Elharrouss et al, 2022 , Fan et al, 2020 , Hu et al, 2022 , Stefano and Comelli, 2021 ). The research studies falling into the aforementioned categories are explained in the subsequent paragraph.…”

Section: Related Workmentioning

confidence: 99%

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Chauhan

Bedi²

2023

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Chauhan

Bedi²

2023

Expert Systems with Applications

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“…When writing this paper, no works addressed the 3D virtual visualisation of segmented COVID-19 lung lesions using Deep Learning approach for Augmented reality rendering to the best of our knowledge. We recently addressed the virtual rendering of COVID-19 lung lesions segmentation in [18] Recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. In this paper, we propose a COVID-19 lung lesions segmentation using U-Net.…”

Section: Introductionmentioning

confidence: 99%

Augmented Reality for COVID-19 Aid Diagnosis: Ct-Scan segmentation based Deep Learning

Amara

Aouf

Kerdjidj

et al. 2022

2022 7th International Conference on Image and Signal Processing and Their Applications (ISPA)

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The virus new variants of Coronavirus disease 2019 (COVID-19) continue to appear, making the situation more challenging and threatening. The COVID-19 pandemic has profoundly affected health systems and medical centres worldwide. The primary clinical tools used in diagnosing patients presenting with respiratory distress and suspected COVID-19 symptoms are radiology examinations. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical specialists. This paper presents an Augmented Reality (AR) tool for COVID-19 aid diagnosis, including Computerised Tomography Ct-scans segmentation based Deep Learning, 3D reconstruction, and AR visualisation. Segmentation is a critical step in AI-based COVID-19 image processing and analysis; we use the popular segmentation networks, including classic U-Net. Quantitative and qualitative evaluation showed reasonable performance of U-Net for lung and COVID-19 lesions segmentation. The AR-COVID-19 aid diagnosis system could be used for medical education professional training and as a support visualisation and reading tool for radiologist.

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