COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network
CMES - Computer Modeling in Engineering and Sciences
; 136(3):2595-2616, 2023.
Article
in English
| Scopus | ID: covidwho-2286023
ABSTRACT
This paper presents a 6-layer customized convolutional neural network model (6L-CNN) to rapidly screen out patients with COVID-19 infection in chest CT images. This model can effectively detect whether the target CT image contains images of pneumonia lesions. In this method, 6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a sample. The results show that the model improves the accuracy of screening out COVID-19 patients. Compared to other methods, the performance is better. In addition, the method can be extended to other similar clinical conditions. © 2023 Tech Science Press. All rights reserved.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
CMES - Computer Modeling in Engineering and Sciences
Year:
2023
Document Type:
Article
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