Öz
Aralık 2019'un sonlarından bu yana Çin'in Wuhan kentinde yeni bir koronavirüs hastalığı Covid-19 kaydedildi ve daha sonra dünya çapında pandemik hale geldi. Covid-19’un, alveollerde bıraktığı hasar ve ilerleyen solunum yetmezliğinin bir sonucu olarak ölümle sonuçlanabilir. Klinik tanı için kullanılan transkripsiyon polimeraz zincir reaksiyonu (RT-PCR) altın standart olmasına rağmen testler yanlış negatifler üretebilir. Ayrıca pandemik durumda RT-PCR test kaynaklarının yetersizliği, tanıyı ve tedaviyi de geciktirebilir. Bu koşullar altında göğüs BT görüntülemesi, Covid-19 hastalarının hem tanı hem de prognozu için değerli bir araç haline gelmiştir. Yakın zamanda BT taramalarında Covid-19 tanısını kolaylaştırmak ve sağlık çalışanlarına yardımcı olmak için derin öğrenme teknikleriyle geliştirilmiş birçok çalışma önerilmiştir. Bu makale, DeOldify kütüphanesini kullanarak renklendirilen BT veri seti ile derin öğrenme tekniklerinden DenseNet121’i kullanarak Covid-19'u, Covid-19 olmayan vakalardan ayırt etmeye odaklanmaktadır. Çalışmamızın sonunda %98’lik doğruluk elde edilmiştir.
Anahtar Kelimeler
Destekleyen Kurum
TÜBİTAK
Proje Numarası
118C364
Kaynakça
- Cifci, M.A. (2020). Deep Learning Model for Diagnosis of Corona Virus Disease from CT Images. International Journal of Scientific & Engineering Research, 11(4), 273–278. http://www.ijser.org
- Antic, J.: Deoldify (2018). https://github.com/jantic/DeOldify. (n.d.).
- Bansal, A., Thakur, G., & Verma, D. (2021). Detection of covid-19 using the ct scan image of lungs. CEUR Workshop Proceedings, 2786, 219–227.
- Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020). Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Computer Methods and Programs in Biomedicine, 196, 105608. https://doi.org/10.1016/j.cmpb.2020.105608
- Bukhari, S. U. khalid, Bukhari, S. S. K., Syed, A., & Shah, S. S. H. (2020). The diagnostic evaluation of convolutional neural network (CNN) for the assessment of chest X-ray of patients infected with COVID-19. MedRxiv. https://doi.org/10.1101/2020.03.26.20044610
- Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016). Densely Connected Convolutional Networks. https://arxiv.org/abs/1608.06993
- Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020). Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. In Journal of Biomolecular Structure and Dynamics. https://doi.org/10.1080/07391102.2020.1788642
- Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196, 105581. https://doi.org/10.1016/j.cmpb.2020.105581
- Lawton, S., & Viriri, S. (2021). Detection of COVID-19 from CT Lung Scans Using Transfer Learning. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/5527923 Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., & Singh, V. (2020). Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons and Fractals, 138, 109944. https://doi.org/10.1016/j.chaos.2020.109944
- Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., & Singh, S. (2020). Deep Transfer Learning Based Classification Model for COVID-19 Disease. IRBM. https://doi.org/10.1016/j.irbm.2020.05.003
- Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N., & Costa, Y. M. G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194, 105532. https://doi.org/10.1016/j.cmpb.2020.105532
- Rahimzadeh, M., & Attar, A. (2020). A NEW MODIFIED DEEP CONVOLUTIONAL NEURAL NETWORK FOR DETECTING COVID-19 FROM X-RAY IMAGES. In arXiv (Vol. 19, p. 100360). arXiv. https://doi.org/10.1016/j.imu.2020.100360
- Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., ... & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). European radiology, 1-9.
- Silva, P., Luz, E., Silva, G., Moreira, G., Silva, R., Lucio, D., & Menotti, D. (2020). COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis. In Informatics in Medicine Unlocked (Vol. 20). https://doi.org/10.1016/j.imu.2020.100427
- Soares, E., Angelov, P., Biaso, S., Froes, M. H., & Abe, D. K. (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. MedRxiv, 1–8.
- Tung-Chen, Y., Martí de Gracia, M., Díez-Tascón, A., Alonso-González, R., Agudo-Fernández, S., Parra-Gordo, M. L., Ossaba-Vélez, S., Rodríguez-Fuertes, P., & Llamas-Fuentes, R. (2020). Correlation between Chest Computed Tomography and Lung Ultrasonography in Patients with Coronavirus Disease 2019 (COVID-19). Ultrasound in Medicine and Biology, 46(11), 2918–2926. https://doi.org/10.1016/j.ultrasmedbio.2020.07.003
Öz
A new coronavirus disease Covid-19 has been recorded in Wuhan, China since late December 2019 and later became a worldwide pandemic. It can result in death as a result of covid-19's damage to the alveoli and progressive respiratory failure. Although transcription polymerase chain reaction (RT-PCR) is the gold standard used for clinical diagnosis, tests can produce false negatives. In addition, in the event of a pandemic, the lack of RT-PCR testing resources may delay diagnosis and treatment. Under these circumstances, Computed Tomography (CT) scans have become a valuable tool for both early diagnosis and prognosis of Covid-19 patients. Recently, many studies developed with deep learning techniques have been proposed to facilitate the diagnosis of Covid-19 in CT scans and to assist healthcare professionals. This paper focuses on distinguishing Covid-19 from non-Covid-19 cases using DenseNet121, one of the deep learning techniques, with the CT dataset colored using the DeOldify library. At the end of our study, an accuracy of 0.98 was obtained.
Anahtar Kelimeler
Proje Numarası
118C364
Kaynakça
- Cifci, M.A. (2020). Deep Learning Model for Diagnosis of Corona Virus Disease from CT Images. International Journal of Scientific & Engineering Research, 11(4), 273–278. http://www.ijser.org
- Antic, J.: Deoldify (2018). https://github.com/jantic/DeOldify. (n.d.).
- Bansal, A., Thakur, G., & Verma, D. (2021). Detection of covid-19 using the ct scan image of lungs. CEUR Workshop Proceedings, 2786, 219–227.
- Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020). Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Computer Methods and Programs in Biomedicine, 196, 105608. https://doi.org/10.1016/j.cmpb.2020.105608
- Bukhari, S. U. khalid, Bukhari, S. S. K., Syed, A., & Shah, S. S. H. (2020). The diagnostic evaluation of convolutional neural network (CNN) for the assessment of chest X-ray of patients infected with COVID-19. MedRxiv. https://doi.org/10.1101/2020.03.26.20044610
- Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016). Densely Connected Convolutional Networks. https://arxiv.org/abs/1608.06993
- Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020). Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. In Journal of Biomolecular Structure and Dynamics. https://doi.org/10.1080/07391102.2020.1788642
- Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196, 105581. https://doi.org/10.1016/j.cmpb.2020.105581
- Lawton, S., & Viriri, S. (2021). Detection of COVID-19 from CT Lung Scans Using Transfer Learning. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/5527923 Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., & Singh, V. (2020). Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons and Fractals, 138, 109944. https://doi.org/10.1016/j.chaos.2020.109944
- Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., & Singh, S. (2020). Deep Transfer Learning Based Classification Model for COVID-19 Disease. IRBM. https://doi.org/10.1016/j.irbm.2020.05.003
- Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N., & Costa, Y. M. G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194, 105532. https://doi.org/10.1016/j.cmpb.2020.105532
- Rahimzadeh, M., & Attar, A. (2020). A NEW MODIFIED DEEP CONVOLUTIONAL NEURAL NETWORK FOR DETECTING COVID-19 FROM X-RAY IMAGES. In arXiv (Vol. 19, p. 100360). arXiv. https://doi.org/10.1016/j.imu.2020.100360
- Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., ... & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). European radiology, 1-9.
- Silva, P., Luz, E., Silva, G., Moreira, G., Silva, R., Lucio, D., & Menotti, D. (2020). COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis. In Informatics in Medicine Unlocked (Vol. 20). https://doi.org/10.1016/j.imu.2020.100427
- Soares, E., Angelov, P., Biaso, S., Froes, M. H., & Abe, D. K. (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. MedRxiv, 1–8.
- Tung-Chen, Y., Martí de Gracia, M., Díez-Tascón, A., Alonso-González, R., Agudo-Fernández, S., Parra-Gordo, M. L., Ossaba-Vélez, S., Rodríguez-Fuertes, P., & Llamas-Fuentes, R. (2020). Correlation between Chest Computed Tomography and Lung Ultrasonography in Patients with Coronavirus Disease 2019 (COVID-19). Ultrasound in Medicine and Biology, 46(11), 2918–2926. https://doi.org/10.1016/j.ultrasmedbio.2020.07.003
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Yapay Zeka |
| Bölüm | PAPERS |
| Yazarlar | |
| Proje Numarası | 118C364 |
| Yayımlanma Tarihi | 20 Ekim 2021 |
| Gönderilme Tarihi | 3 Eylül 2021 |
| Kabul Tarihi | 16 Eylül 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special |
Cited By
X-RAY GÖĞÜS GÖRÜNTÜLERİNİN GÖRÜNTÜ DÖNÜŞTÜRÜCÜLER İLE SINIFLANDIRILMASI VE COVİD-19 TESPİTİ
Uludağ University Journal of The Faculty of Engineering
https://doi.org/10.17482/uumfd.1179180
Covid19 Yayilimini Azaltmak İçin Yüz Maskesinin Evrişimsel Sinir Aği Modelleri ile Tespiti
Journal of the Institute of Science and Technology
https://doi.org/10.21597/jist.1251058
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