Automated detection of Covid-19 disease using deep fused features from chest radiography images
Yazarlar (4)
Emine Uçar İskenderun Teknik Üniversitesi, Türkiye
Doç. Dr. Ümit Atila Gazi Üniversitesi, Türkiye
Murat Uçar İskenderun Teknik Üniversitesi, Türkiye
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Biomedical Signal Processing and Control (Q2) | ||
| Dergi ISSN | 1746-8094 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 08-2021 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-08-2021 |
| Cilt / Sayı / Sayfa | 69 / 1 / 1–10 | DOI | 10.1016/j.bspc.2021.102862 |
| Makale Linki | 10.1016/j.bspc.2021.102862 | ||
| Özet |
| The health systems of many countries are desperate in the face of Covid-19, which has become a pandemic worldwide and caused the death of hundreds of thousands of people. In order to keep Covid-19, which has a very high propagation rate, under control, it is necessary to develop faster, low-cost and highly accurate methods, rather than a costly Polymerase Chain Reaction test that can yield results in a few hours. In this study, a deep learning-based approach that can detect Covid-19 quickly and with high accuracy on X-ray images, which are common in every hospital and can be obtained at low cost, was proposed. Deep features were extracted from X-Ray images in RGB, CIE Lab and RGB CIE color spaces using DenseNet121 and EfficientNet B0 pre-trained deep learning architectures and then obtained features were fed into a two-stage classifier approach. Each of the classifiers in the proposed approach … |
| Anahtar Kelimeler |
| Automatic medical diagnosis | Bi-LSTM | Covid-19 | Deep learning | Pneumonia | X-ray |
Science Direct
Automated detection of Covid-19 disease using deep fused features from chest radiography images
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 17 |
| Google Scholar | 20 |