A Novel Convolutional Neural Network-Based Segmentation Model for Lung CT Scan Images Affected by COVID-19
4th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021
; 936:837-850, 2022.
Article
in English
| Scopus | ID: covidwho-2148682
ABSTRACT
In recent times, the detection of COVID by lung CT scan images has become an active field of research due to the increase in the number of COVID cases worldwide. COVID causes lesion-based damage in the lungs which can be easily analyzed by a CT scan image. The proposed methodology uses a publicly available database of lung computed tomography (CT) scan images collected from 297 subjects having 8739 scans and thereby apply a Covi-Net model for lesion-based segmentation and thereby COVID detection. The Covi-Net model is an extension of U-Net model used for biomedical image classification. The model outperformed related algorithms with a dice value of 0.886. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021
Year:
2022
Document Type:
Article
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