Makale detayı · 2021
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks
- Yıl
- 2021
- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- YÖKSİS dergi adı Pattern Analysis and Applications
- Katalog eşleşmesi (ISSN) Pattern Analysis and Applications
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
OpenAlex · İngilizce
The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.
Konular
Atıflar
OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.
1.358 atıf
OpenAlex cited_by_count (önbellek / veritabanı)
Yerel katalogda bu makaleye atıf yapan 126 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).
- COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble 2021
- A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic 2020
- A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic 2020
- Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images 2021
- Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images 2021
- VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm 2022
- A systematic review on AI/ML approaches against COVID-19 outbreak 2021
- Citrus Disease Detection and Classification Using Based on Convolution Deep Neural Network 2022
- A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection 2023
- Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images 2021