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DETECTION OF CORONAVIRUS DISEASE USING WAVELET CONVOLUTIONAL NEURAL NETWORK METHOD

Yıl 2023, Cilt: 26 Sayı: 1, 203 - 212, 15.03.2023

Abidin Çalışkan

https://doi.org/10.17780/ksujes.1208283

Öz

Coronavirus (Covid-19) is a type of RNA-type virus that has been felt around the world since 2019 and has deadly consequences. The Covid-19 virus, usually shows its effectiveness in the lungs and causes various respiratory tract infections. In this study, a new artificial intelligence-based Convolutional Neural Network (CNN) model that can diagnose Covid-19 has been proposed. Spatial and spectral approaches are frequently used in image analysis and operations such as object identification. CNN models, on the other hand, generally process images in spatial areas and complete the training process by using the attributes obtained from there. In order to add a different perspective to the CNN model proposed in this study, the spatial and spectral processing of the input images was carried out. Thus, it was possible to extract different multi-resolution features. The missing parts of the multi-resolution analysis steps were completed using the so-called wavelet transform method. As a result, the overall accuracy of 98.48% was achieved in the experimental analyzes performed with the proposed approach, Wavelet CNN (W-CNN).

Anahtar Kelimeler

Deep learning convolutional neural network wavelet neural network respiratory diseases coronavirus.

Kaynakça

  • Abdulkareem, K. H., Mostafa, S. A., Al-Qudsy, Z. N., Mohammed, M. A., Al-Waisy, A. S., Kadry, S., Lee, J., & Nam, Y. (2022). Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models. Journal of Healthcare Engineering, 1-13. https://doi.org/10.1155/2022/5329014.
  • AbdulQader, D. A., Saadoon, A. T., Naser, M. T., & Jabbar, A. H. (2023). Classification of COVID-19 from CT chest images using convolutional wavelet neural network. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 1078-1085. https://doi.org/10.11591/ijece.v13i1.
  • Alyasseri, Z. A. A., Al‐Betar, M. A., Doush, I. A., Awadallah, M. A., Abasi, A. K., Makhadmeh, S. N., Alomari, O. A., Abdulkareem K. H., Adam A., Damasevicius R., Mohammed M. A., & Zitar R. A. (2022). Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches. Expert systems, 39(3), e12759. https://doi.org/10.1111/exsy.12759.
  • Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and engineering sciences in medicine, 43(2), 635-640. https://doi.org/10.1007/s13246-020-00865-4.
  • Bhardwaj, P., & Kaur, A. (2021). A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality. International Journal of Imaging Systems and Technology, 31(4), 1775-1791. https://doi.org/10.1002/ima.22627.
  • Çalışkan, A. (2022). Classification of Tympanic Membrane Images based on VGG16 Model. Kocaeli Journal of Science and Engineering, 5(1), 105-111. https://doi.org/10.34088/kojose.1081402.
  • Deb, S.D., Jha, R.K., Jha, K. & Tripathi, P.S. (2022). A multi model ensemble based deep convolution neural network structure for detection of COVID19. Biomedical Signal Processing and Control, 71, 103126. https://doi.org/10.1016/j.bspc.2021.103126.
  • Fan, X., Feng, X., Dong, Y., & Hou, H. (2022). COVID-19 CT image recognition algorithm based on transformer and CNN. Displays, 72, 102150. https://doi.org/10.1016/j.displa.2022.102150.
  • Fujieda, S., Takayama, K., & Hachisuka, T. (2018). Wavelet convolutional neural networks. arXiv preprint arXiv:1805.08620. https://doi.org/10.48550/arXiv.1805.08620.
  • Google Colab Notebooks- Colaboratory, Google. (2021). https://colab.research.google.com/notebooks/intro.ipynb/ Erişim Tarihi 09.06.2021.
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377. https://doi.org/10.1016/j.patcog.2017.10.013.
  • Huang, X. (2021). COVID-19 Image Diagnosis on CT Images Using Deep Learning. In 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), IEEE, 77-80. https://doi.org/10.1109/ICAICE54393.2021.00023.
  • Jin, G., Liu, C. & Chen, X. (2022). An efficient deep neural network framework for COVID-19 lung infection segmentation. Information Sciences, 612, 745-758. https://doi.org/10.1016/j.ins.2022.08.059.
  • Karthik, R., Menaka, R., & Hariharan, M. (2021). Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN. Applied Soft Computing, 99, 106744. https://doi.org/10.1016/j.asoc.2020.106744.
  • Kini, A. S., Gopal Reddy, A. N., Kaur, M., Satheesh, S., Singh, J., Martinetz, T., & Alshazly, H. (2022). Ensemble deep learning and internet of things-based automated COVID-19 diagnosis framework. Contrast Media & Molecular Imaging, 2022, 7377502. https://doi.org/10.1155/2022/7377502.
  • Le Dinh, T., Lee, S.H., Kwon, S.G., & Kwon, K.R. (2022). COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks. Applied Sciences, 12(10), 4861. https://doi.org/10.3390/app12104861.
  • Maghdid, H. S., Asaad, A. T., Ghafoor, K. Z., Sadiq, A.S., Mirjalili, S., & Khan, M. K. (2021). Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In Multimodal image exploitation and learning 2021, 11734, 99-110. https://doi.org/10.1117/12.2588672
  • Muneer, A., Fati, S. M., Akbar, N. A., Agustriawan, D., & Wahyudi, S. T. (2022). iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning. Journal of King Saud University-Computer and Information Sciences, 34(9), 7419-7432. https://doi.org/10.1016/j.jksuci.2021.10.001
  • Narin, A., Kaya, C., & Pamuk, Z. (2021). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24(3), 1207-1220. https://doi.org/10.1007/s10044-021-00984-y
  • Nishio, M., Noguchi, S., Matsuo, H., & Murakami, T. (2020). Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods. Scientific reports, 10(1), 1-6. https://doi.org/10.1038/s41598-020-74539-2
  • Nasiri, H., & Hasani, S. (2022). Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost. Radiography, 28, 732-738. https://doi.org/10.1016/j.radi.2022.03.011
  • Nneji, G. U., Cai, J., Jianhua, D., Chikwendu, I. A., Oluwasanmi, A., James, E. C., & Mgbejime, G. T. (2021). Enhancing low quality in radiograph datasets using wavelet transform convolutional neural network and generative adversarial network for COVID-19 identification. In 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), IEEE, (pp. 146-151). https://doi.org/10.1109/PRAI53619.2021.9551043
  • O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. https://doi.org/10.48550/arXiv.1511.08458
  • Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., Bhardwaj, P., & Singh, V. (2020). A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos, Solitons & Fractals, 140, 110190. https://doi.org/10.1016/j.chaos.2020.110190
  • Raikote, P. (2019). Covid-19 Image Dataset. https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset/ Erişim Tarihi 20.05.2022.
  • Sarvamangala, D. R., & Kulkarni, R. V. (2021). Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence, 1-22. https://doi.org/10.1007/s12065-020-00540-3
  • Shahin, O. R., Abd El-Aziz, R. M. & Taloba, A. I. (2022). Detection and classification of Covid-19 in CT-lungs screening using machine learning techniques. Journal of Interdisciplinary Mathematics, 25(3), 791-813. https://doi.org/10.1080/09720502.2021.2015097
  • Shorfuzzaman, M., & Hossain, M. S. (2021). MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients. Pattern recognition, 113, 107700. https://doi.org/10.1016/j.patcog.2020.107700
  • Singh, K. K., & Singh, A. (2021). Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network. Big Data Mining and Analytics, 4(2), 84-93. https://doi.org/10.26599/BDMA.2020.9020012
  • Subramanian, N., Elharrouss, O., Al-Maadeed, S., & Chowdhury, M. (2022). A review of deep learning-based detection methods for COVID-19. Computers in Biology and Medicine, 105233. https://doi.org/10.1016/j.compbiomed.2022.105233
  • Sunitha, G., Arunachalam, R., Abd‐Elnaby, M., Eid, M. M., & Rashed, A. N. Z. (2022). A comparative analysis of deep neural network architectures for the dynamic diagnosis of COVID‐19 based on acoustic cough features. International Journal of Imaging Systems and Technology, 32(5), 1433-1446. https://doi.org/10.1002/ima.22749
  • Toğaçar, M. (2021). X-ışınlı Göğüs İmgelerini Kullanarak Solunum Yolu Hastalıklarının Tespitinde Kuantum Transfer Öğrenme Modelinin Rolü. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(5), 1754-1765. https://doi.org/10.29130/dubited.903358
  • Toğaçar, M., Ergen, B., & Cömert, Z. (2021). Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks. Medical & Biological Engineering & Computing, 59(1), 57-70. https://doi.org/10.1007/s11517-020-02290-x
  • Tran, A. T., Luong, T. D., Ha, C. C., Hoang, D. T., & Tran, T. L. (2021). Secure Inference via Deep Learning as a Service without Privacy Leakage. In 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), IEEE, 1-6. https://doi.org/10.1109/RIVF51545.2021.9642089
  • Umer, M., Ashraf, I., Ullah, S., Mehmood, A., & Choi, G. S. (2022). COVINet: A convolutional neural network approach for predicting COVID-19 from chest X-ray images. Journal of Ambient Intelligence and Humanized Computing, 13(1), 535-547. https://doi.org/10.1007/s12652-021-02917-3
  • Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4), 611-629. https://doi.org/10.1007/s13244-018-0639-9
  • Yu, C. S., Chang, S. S., Chang, T. H., Wu, J. L., Lin, Y. J., Chien, H. F., & Chen, R. J. (2021). A COVID-19 pandemic artificial intelligence–based system with deep learning forecasting and automatic statistical data acquisition: development and implementation study. Journal of medical Internet research, 23(5), e27806. https://doi.org/10.2196/27806
  • Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12. https://doi.org/10.1038/s41598-020-76550-z
  • Wehbe, R. M., Sheng, J., Dutta, S., Chai, S., Dravid, A., Barutcu, S., Wu, Y., Cantrell, D. R., Xiao, N., Allen, B. D., MacNealy, G. A., Savas H., Agrawal, R., Parekh, N., & Katsaggelos, A. K. (2021). DeepCOVID-XR: an artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large US clinical data set. Radiology, 299(1), E167. https://doi.org/10.1148/radiol.2020203511
  • Zhan, J. X., & Santos-Paulino, A. U. (2021). Investing in the Sustainable Development Goals: Mobilization, channeling, and impact. Journal of International Business Policy, 4(1), 166-183. https://doi.org/10.1057/s42214-020-00093-3
  • Zhang, Y. D., Satapathy, S. C., Zhang, X., & Wang, S. H. (2021). Covid-19 diagnosis via DenseNet and optimization of transfer learning setting. Cognitive computation, 1-17. https://doi.org/10.1007/s12559-020-09776-8

DALGACIK EVRİŞİMSEL SİNİR AĞI YÖNTEMİ İLE KORONAVİRÜS HASTALIĞININ TESPİTİ

Yıl 2023, Cilt: 26 Sayı: 1, 203 - 212, 15.03.2023

Abidin Çalışkan

https://doi.org/10.17780/ksujes.1208283

Öz

Koronavirüs (Kovid-19), 2019 yılından itibaren dünya genelinde hissedilen ve ölümcül sonuçları olan RNA tipi bir virüs türüdür. Kovid-19 virüsü, genellikle akciğerde etkinliğini göstermekte olup, çeşitli solunum yolu enfeksiyonlarına neden olmaktadır. Bu çalışmada, Kovid-19 tanısını gerçekleştirebilen yapay zekâ tabanlı yeni bir Evrişimsel Sinir Ağı (ESA) modeli önerilmiştir. Uzamsal ve spektral yaklaşımlar, görüntü analizlerinde ve nesne tanımlama gibi işlemlerde sıkça kullanılmaktadır. ESA modellerinde genellikle görüntüler uzamsal alanlarda işlenir ve eğitim sürecini buradan elde ettikleri öznitelikleri kullanarak tamamlarlar. Bu çalışmada önerilen ESA modeline farklı bir bakış açısı katabilmek için girdi görüntülerini mekânsal ve spektral olarak işlenmesi gerçekleştirildi. Böylece çok çözünürlüklü farklı özniteliklerin çıkartılması sağlandı. Çok çözünürlüklü analiz adımlarının eksik kısımlarını dalgacık dönüşümü denilen yöntem kullanılarak tamamlandı. Sonuç olarak, önerilen yaklaşım olan Dalgacık ESA (D-ESA) ile gerçekleştirilen deneysel analizlerde %98,48 genel doğruluk başarısı elde edilmiştir

Anahtar Kelimeler

Derin öğrenme evrişimsel sinir ağı dalgacık sinir ağı solunum hastalıkları koronavirüs.

Kaynakça

  • Abdulkareem, K. H., Mostafa, S. A., Al-Qudsy, Z. N., Mohammed, M. A., Al-Waisy, A. S., Kadry, S., Lee, J., & Nam, Y. (2022). Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models. Journal of Healthcare Engineering, 1-13. https://doi.org/10.1155/2022/5329014.
  • AbdulQader, D. A., Saadoon, A. T., Naser, M. T., & Jabbar, A. H. (2023). Classification of COVID-19 from CT chest images using convolutional wavelet neural network. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 1078-1085. https://doi.org/10.11591/ijece.v13i1.
  • Alyasseri, Z. A. A., Al‐Betar, M. A., Doush, I. A., Awadallah, M. A., Abasi, A. K., Makhadmeh, S. N., Alomari, O. A., Abdulkareem K. H., Adam A., Damasevicius R., Mohammed M. A., & Zitar R. A. (2022). Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches. Expert systems, 39(3), e12759. https://doi.org/10.1111/exsy.12759.
  • Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and engineering sciences in medicine, 43(2), 635-640. https://doi.org/10.1007/s13246-020-00865-4.
  • Bhardwaj, P., & Kaur, A. (2021). A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality. International Journal of Imaging Systems and Technology, 31(4), 1775-1791. https://doi.org/10.1002/ima.22627.
  • Çalışkan, A. (2022). Classification of Tympanic Membrane Images based on VGG16 Model. Kocaeli Journal of Science and Engineering, 5(1), 105-111. https://doi.org/10.34088/kojose.1081402.
  • Deb, S.D., Jha, R.K., Jha, K. & Tripathi, P.S. (2022). A multi model ensemble based deep convolution neural network structure for detection of COVID19. Biomedical Signal Processing and Control, 71, 103126. https://doi.org/10.1016/j.bspc.2021.103126.
  • Fan, X., Feng, X., Dong, Y., & Hou, H. (2022). COVID-19 CT image recognition algorithm based on transformer and CNN. Displays, 72, 102150. https://doi.org/10.1016/j.displa.2022.102150.
  • Fujieda, S., Takayama, K., & Hachisuka, T. (2018). Wavelet convolutional neural networks. arXiv preprint arXiv:1805.08620. https://doi.org/10.48550/arXiv.1805.08620.
  • Google Colab Notebooks- Colaboratory, Google. (2021). https://colab.research.google.com/notebooks/intro.ipynb/ Erişim Tarihi 09.06.2021.
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377. https://doi.org/10.1016/j.patcog.2017.10.013.
  • Huang, X. (2021). COVID-19 Image Diagnosis on CT Images Using Deep Learning. In 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), IEEE, 77-80. https://doi.org/10.1109/ICAICE54393.2021.00023.
  • Jin, G., Liu, C. & Chen, X. (2022). An efficient deep neural network framework for COVID-19 lung infection segmentation. Information Sciences, 612, 745-758. https://doi.org/10.1016/j.ins.2022.08.059.
  • Karthik, R., Menaka, R., & Hariharan, M. (2021). Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN. Applied Soft Computing, 99, 106744. https://doi.org/10.1016/j.asoc.2020.106744.
  • Kini, A. S., Gopal Reddy, A. N., Kaur, M., Satheesh, S., Singh, J., Martinetz, T., & Alshazly, H. (2022). Ensemble deep learning and internet of things-based automated COVID-19 diagnosis framework. Contrast Media & Molecular Imaging, 2022, 7377502. https://doi.org/10.1155/2022/7377502.
  • Le Dinh, T., Lee, S.H., Kwon, S.G., & Kwon, K.R. (2022). COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks. Applied Sciences, 12(10), 4861. https://doi.org/10.3390/app12104861.
  • Maghdid, H. S., Asaad, A. T., Ghafoor, K. Z., Sadiq, A.S., Mirjalili, S., & Khan, M. K. (2021). Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In Multimodal image exploitation and learning 2021, 11734, 99-110. https://doi.org/10.1117/12.2588672
  • Muneer, A., Fati, S. M., Akbar, N. A., Agustriawan, D., & Wahyudi, S. T. (2022). iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning. Journal of King Saud University-Computer and Information Sciences, 34(9), 7419-7432. https://doi.org/10.1016/j.jksuci.2021.10.001
  • Narin, A., Kaya, C., & Pamuk, Z. (2021). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24(3), 1207-1220. https://doi.org/10.1007/s10044-021-00984-y
  • Nishio, M., Noguchi, S., Matsuo, H., & Murakami, T. (2020). Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods. Scientific reports, 10(1), 1-6. https://doi.org/10.1038/s41598-020-74539-2
  • Nasiri, H., & Hasani, S. (2022). Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost. Radiography, 28, 732-738. https://doi.org/10.1016/j.radi.2022.03.011
  • Nneji, G. U., Cai, J., Jianhua, D., Chikwendu, I. A., Oluwasanmi, A., James, E. C., & Mgbejime, G. T. (2021). Enhancing low quality in radiograph datasets using wavelet transform convolutional neural network and generative adversarial network for COVID-19 identification. In 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), IEEE, (pp. 146-151). https://doi.org/10.1109/PRAI53619.2021.9551043
  • O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. https://doi.org/10.48550/arXiv.1511.08458
  • Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., Bhardwaj, P., & Singh, V. (2020). A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos, Solitons & Fractals, 140, 110190. https://doi.org/10.1016/j.chaos.2020.110190
  • Raikote, P. (2019). Covid-19 Image Dataset. https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset/ Erişim Tarihi 20.05.2022.
  • Sarvamangala, D. R., & Kulkarni, R. V. (2021). Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence, 1-22. https://doi.org/10.1007/s12065-020-00540-3
  • Shahin, O. R., Abd El-Aziz, R. M. & Taloba, A. I. (2022). Detection and classification of Covid-19 in CT-lungs screening using machine learning techniques. Journal of Interdisciplinary Mathematics, 25(3), 791-813. https://doi.org/10.1080/09720502.2021.2015097
  • Shorfuzzaman, M., & Hossain, M. S. (2021). MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients. Pattern recognition, 113, 107700. https://doi.org/10.1016/j.patcog.2020.107700
  • Singh, K. K., & Singh, A. (2021). Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network. Big Data Mining and Analytics, 4(2), 84-93. https://doi.org/10.26599/BDMA.2020.9020012
  • Subramanian, N., Elharrouss, O., Al-Maadeed, S., & Chowdhury, M. (2022). A review of deep learning-based detection methods for COVID-19. Computers in Biology and Medicine, 105233. https://doi.org/10.1016/j.compbiomed.2022.105233
  • Sunitha, G., Arunachalam, R., Abd‐Elnaby, M., Eid, M. M., & Rashed, A. N. Z. (2022). A comparative analysis of deep neural network architectures for the dynamic diagnosis of COVID‐19 based on acoustic cough features. International Journal of Imaging Systems and Technology, 32(5), 1433-1446. https://doi.org/10.1002/ima.22749
  • Toğaçar, M. (2021). X-ışınlı Göğüs İmgelerini Kullanarak Solunum Yolu Hastalıklarının Tespitinde Kuantum Transfer Öğrenme Modelinin Rolü. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(5), 1754-1765. https://doi.org/10.29130/dubited.903358
  • Toğaçar, M., Ergen, B., & Cömert, Z. (2021). Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks. Medical & Biological Engineering & Computing, 59(1), 57-70. https://doi.org/10.1007/s11517-020-02290-x
  • Tran, A. T., Luong, T. D., Ha, C. C., Hoang, D. T., & Tran, T. L. (2021). Secure Inference via Deep Learning as a Service without Privacy Leakage. In 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), IEEE, 1-6. https://doi.org/10.1109/RIVF51545.2021.9642089
  • Umer, M., Ashraf, I., Ullah, S., Mehmood, A., & Choi, G. S. (2022). COVINet: A convolutional neural network approach for predicting COVID-19 from chest X-ray images. Journal of Ambient Intelligence and Humanized Computing, 13(1), 535-547. https://doi.org/10.1007/s12652-021-02917-3
  • Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4), 611-629. https://doi.org/10.1007/s13244-018-0639-9
  • Yu, C. S., Chang, S. S., Chang, T. H., Wu, J. L., Lin, Y. J., Chien, H. F., & Chen, R. J. (2021). A COVID-19 pandemic artificial intelligence–based system with deep learning forecasting and automatic statistical data acquisition: development and implementation study. Journal of medical Internet research, 23(5), e27806. https://doi.org/10.2196/27806
  • Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12. https://doi.org/10.1038/s41598-020-76550-z
  • Wehbe, R. M., Sheng, J., Dutta, S., Chai, S., Dravid, A., Barutcu, S., Wu, Y., Cantrell, D. R., Xiao, N., Allen, B. D., MacNealy, G. A., Savas H., Agrawal, R., Parekh, N., & Katsaggelos, A. K. (2021). DeepCOVID-XR: an artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large US clinical data set. Radiology, 299(1), E167. https://doi.org/10.1148/radiol.2020203511
  • Zhan, J. X., & Santos-Paulino, A. U. (2021). Investing in the Sustainable Development Goals: Mobilization, channeling, and impact. Journal of International Business Policy, 4(1), 166-183. https://doi.org/10.1057/s42214-020-00093-3
  • Zhang, Y. D., Satapathy, S. C., Zhang, X., & Wang, S. H. (2021). Covid-19 diagnosis via DenseNet and optimization of transfer learning setting. Cognitive computation, 1-17. https://doi.org/10.1007/s12559-020-09776-8
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Bilgisayar Mühendisliği
Yazarlar

Abidin Çalışkan 0000-0001-5039-6400

Yayımlanma Tarihi 15 Mart 2023
Gönderilme Tarihi 21 Kasım 2022
Yayımlandığı Sayı Yıl 2023Cilt: 26 Sayı: 1

Kaynak Göster

APA Çalışkan, A. (2023). DALGACIK EVRİŞİMSEL SİNİR AĞI YÖNTEMİ İLE KORONAVİRÜS HASTALIĞININ TESPİTİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(1), 203-212. https://doi.org/10.17780/ksujes.1208283
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