Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images
Yazarlar (2)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
Baha Şen
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Interdisciplinary Sciences Computational Life Sciences (Q2) | ||
| Dergi ISSN | 1913-2751 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2022 |
| Kabul Tarihi | 12-07-2021 | Yayınlanma Tarihi | 27-07-2021 |
| Cilt / Sayı / Sayfa | 14 / 1 / 89–100 | DOI | 10.1007/s12539-021-00463-2 |
| Makale Linki | 10.1007/s12539-021-00463-2 | ||
| Özet |
| Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features … |
| Anahtar Kelimeler |
| Artifcial intelligence | Bi-LSTM | Concatenated deep features | Covid-19 | Deep learning | X-ray imaging |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 7 |
| Google Scholar | 8 |