Deep Residual Learning based on ResNet50 for COVID-19 Recognition in Lung CT Images*

被引：0

作者：

Ferjaoui, Radhia ^{[1
]}

Cherni, Mohamed Ali ^{[2
]}

Abidi, Fathia ^{[3
]}

Zidi, Asma ^{[3
]}

机构：

[1] Univ Tunis El Manar, ISTMT, Lab Rech Biophys & Technol Med LRBTM, Tunis, Tunisia

[2] Univ Tunis, LR13 ES03 SIME, ENSIT, Tunis 1008, Tunisia

[3] Inst Salah Azaiez, Serv Imagerie Med, Tunis 1006, Tunisia

来源：

2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) | 2022年

关键词：

Covid-19; Deep learning; Lung CT; Machine learning; Residual learning; Resnet50;

D O I：

10.1109/CODIT55151.2022.9804094

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the start of 2020, the world witnessed the spread of Coronavirus disease (COVID-19). We aim in this work to employ artificial intelligence (AI) to develop a computeraided diagnosis system (CAD) in order to automatically detect COVID-19 cases and differentiate them from normal and community-acquired pneumonia (CAP) cases through the use of lung Computed Tomography (CT) images and then evaluate its performance. Deep residual learning offers a wide variety of algorithms that helps in classification problems. We apply in this work a ResNet50 based model to recognize Covid-19 cases. Extensive analysis based on an international dataset (24256 images of 304 patients) proved that the ResNet50-optimized model can recognize COVID-19 through the use of CT images with 82% accuracy, 90% recall, 65% precision, and 76% of F1.Score.

引用

页码：407 / 412

页数：6

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