Koronavirüs hastalığını tespit etmek için CNN tabanlı bir hibrit model

Öz

Bu yazıda, COVID-19 hastalığı için hibrit bir sınıflandırma tekniği önerilmektedir. Önerilen model, iki sınıflı sınıflandırma problemini çözmektedir (covid, normal). Bu çalışmada, üstün derin öğrenme ve makine öğrenimi sınıflandırıcılarını entegre eden hibrit modeller sunduk: Evrişimsel Sinir Ağ (CNN) ve Karar Destek Makinesi (SVM), CNN ve AdaBoost, CNN ve K En Yakın Komşu (kNN), CNN ve Çok Katmanlı Algılayıcı ( MLP), CNN ve Naive Bayes (NB). Bu modellerde CNN, eğitilebilir bir derin özellik çıkarıcı olarak çalışır ve SVM, AdaBoost, kNN, MLP, NB bir tanıyıcı olarak davranır. Tüm deneyler, COVID-CT ve SARS-CoV-2 CT birleşik görüntü veri kümeleri üzerinde gerçekleştirilmiştir. Sonuç olarak, önerilen hibrit yöntemler duyarlılık, doğruluk, kesinlik, F1 puanı, AUC puanı, özgüllük, FPR, FDR ve FNR açısından karşılaştırılmıştır. CNN + SVM, CNN + MLP ve CNN + kNN, diğer modellere göre sırasıyla daha iyi performans gösteren sonuçlar elde etmiştir. Ayrıca, CNN + SVM en iyi performansı gösterdi (% 85,85 hassasiyet,% 85,86 kesinlik,% 85,86 doğruluk,% 85,85 F1 skoru,% 85,85 AUC skoru,% 86,47 özgüllük,% 13,52 FPR,% 13,86 FDR ve% 14,76 FNR) . Sonuçlar incelendiğinde, önerilen hibrit sistemin COVID-19'u tespit etmede etkili olduğu görülüyor. Ayrıca, önerilen hibrit sistemin performansı, literatürdeki COVID-CT ve SARS-CoV-2 CT birleşik görüntü veri kümelerinde bulunan başarılı çalışmalardan daha iyidir.

Anahtar Kelimeler

• Covid-19
• hibrit modeller
• derin öğrenme
• CNN

A CNN-based hybrid model to detect Coronavirus disease

Öz

In this paper, a hybrid classification technique for COVID-19 disease is proposed. The proposed model solves the two-class classification problem (covid, normal). In this study, we have presented hybrid models integrating superior deep learning and machine learning classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), CNN and AdaBoost, CNN and K Nearest Neighborhood (kNN), CNN and Multilayer Perceptron (MLP), CNN and Naive Bayes (NB). In these models, CNN performs as a trainable deep feature extractor, and SVM, AdaBoost, kNN, MLP, NB behave as a recognizer. All experiments have been performed on COVID-CT and SARS-CoV-2 CT combined image datasets. As a result, proposed hybrid methods have been compared in terms of sensitivity, accuracy, precision, F1-score, AUC-score, specificity, FPR, FDR, and FNR. CNN+SVM, CNN+MLP, and CNN+kNN have achieved outperforming results according to the other models, respectively. Also, CNN+SVM performed the best (achieving 85.85% sensitivity, 85.86% precision, 85.86% accuracy, 85.85% F1-score, 85.85% AUC score, 86.47% specificity, 13.52% FPR, 13.86% FDR, and 14.76% FNR). When the results are examined, the proposed hybrid system is seen to be efficient to detect COVID-19. Also, the performance of the proposed hybrid system is better than the successful studies found on COVID-CT and SARS-CoV-2 CT combined image datasets in the literature.

Anahtar Kelimeler

• Covid-19
• hybrid models
• deep learning
• CNN

Kaynakça

1. Karakuş, A. T. “The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic”. Sakarya University Journal of Computer and Information Sciences, 3(3), 201-209, 2020.
2. Wu X., et al.. “Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study”. European Journal of Radiology, 109041, 2020.
3. Jin C., et al.. “Development and Evaluation of an AI System for COVID-19 Diagnosis”. medRxiv, 2020.
4. Javaheri T., et al. “Covidctnet: An open-source deep learning approach to identify covid-19 using ct image”. arXiv preprint arXiv:2005.03059, 2020.
5. Jin S., et al.. “AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks”. medRxiv, 2020.
6. Chen J., et al.. “Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study”. MedRxiv, 2020.
7. Ardakani A. A., Kanafi A. R., Acharya U. R., Khadem N., & Mohammadi A. “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks”. Computers in Biology and Medicine, 103795, 2020.
8. He X., Yang X., Zhang S., Zhao J., Zhang Y., Xing E., & Xie P. “Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans”. medRxiv, 2020.

9. Wang S., et al. “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)”. MedRxiv, 2020.
10. Song Y., et al. “Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images”. medRxiv, 2020.
11. Zheng C., et al. “Deep learning-based detection for COVID-19 from chest CT using weak label”. medRxiv, 2020.
12. Singh D., Kumar V., & Kaur M. “Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks”. European Journal of Clinical Microbiology & Infectious Diseases, 1-11, 2020.
13. Farid A. A., Selim G. I., Awad H., & Khater A. “A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19).” Int. J. Sci. Eng. Res, 11(3), 1-9, 2020.
14. Wang S., et al.. “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)”. medRxiv, 2020.
15. Song Y., et al.. “Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images”. medRxiv, 2020.
16. Yang X., et al. “COVID-CT-dataset: a CT scan dataset about COVID-19”. ArXiv e-prints, arXiv-2003, 2020.
17. COVID-CT, https://github.com/UCSD-AI4H/COVID-CT (08.09.2020).
18. Soares E., Angelov P., Biaso S., Froes M. H., & Abe D. K. “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification”. medRxiv,2020.
19. SARS-COV-2 Ct-Scan Dataset A large dataset of CT scans for SARS-CoV-2 (COVID-19) identification, https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset (08.09.2020).
20. Ciresan D. C., Meier U., Masci J., Gambardella L. M., & Schmidhuber J. “Flexible, high performance convolutional neural networks for image classification”. In Twenty-second international joint conference on artificial intelligence, 2011.
21. Yildirim M., & Cinar A. “A Deep Learning Based Hybrid Approach for COVID-19 Disease Detections”. Traitement du Signal, 37(3), 461-468, 2020.
22. Cortes C. “WSupport-vector network”. Machine learning, 20, 1-25, 1995.
23. Freund Y., & Schapire R. E. “A decision-theoretic generalization of on-line learning and an application to boosting”. Journal of computer and system sciences, 55(1), 119-139, 1997.
24. Cover T., & Hart P. “Nearest neighbor pattern classification”. IEEE transactions on information theory, 13(1), 21-27, 1967.
25. Witten I. H., & Frank E. “Data mining: practical machine learning tools and techniques with Java implementations”. Acm Sigmod Record, 31(1), 76-77, 2002.
26. Russell S., & Norvig P. “Artificial intelligence: a modern approach”. 2002.
27. Polsinelli M., Cinque L., & Placidi G. “A Light CNN for detecting COVID-19 from CT scans f the chest. arXiv preprint arXiv:2004.12837”. 2020.
28. Silva P., Luz E., Silva G., Moreira G., Silva R., Lucio D., & Menotti D. “COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis”. Informatics in Medicine Unlocked, 20, 100427, 2020.
29. Wang Z., Liu Q., & Dou Q. “Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification”. IEEE Journal of Biomedical and Health Informatics, 24(10), 2806-2813, 2020.
30. Saeedi A., Saeedi M., & Maghsoudi A. “A novel and reliable deep learning web-based tool to detect COVID-19 infection form chest CT-scan”. arXiv preprint arXiv:2006.14419, 2020.