A CNN-based hybrid model to detect Coronavirus disease
Yıl: 2021 Cilt: 0 Sayı: 27 Sayfa Aralığı: 66 - 73 Metin Dili: İngilizce DOI: 10.31590/ejosat.936820 İndeks Tarihi: 24-05-2023
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 Neighbour (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 recognizers. 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. Also, CNN+SVM performed the best. 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 Kelime: 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 göstermiştir. Sonuçlar incelendiğinde, önerilen hibrit sistemin COVID-19'u tespit etmede etkili olduğu görülmektedir. Ayrıca, önerilen hibrit sistemin performansı, literatürdeki COVID-CT ve SARS-CoV-2 CT birleşik görüntü veri kümelerinde bulunan başarılı çalışmalardan daha iyidir.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | ERDEM E, Aydın T (2021). A CNN-based hybrid model to detect Coronavirus disease. , 66 - 73. 10.31590/ejosat.936820 |
| Chicago | ERDEM EBRU,Aydın Tolga A CNN-based hybrid model to detect Coronavirus disease. (2021): 66 - 73. 10.31590/ejosat.936820 |
| MLA | ERDEM EBRU,Aydın Tolga A CNN-based hybrid model to detect Coronavirus disease. , 2021, ss.66 - 73. 10.31590/ejosat.936820 |
| AMA | ERDEM E,Aydın T A CNN-based hybrid model to detect Coronavirus disease. . 2021; 66 - 73. 10.31590/ejosat.936820 |
| Vancouver | ERDEM E,Aydın T A CNN-based hybrid model to detect Coronavirus disease. . 2021; 66 - 73. 10.31590/ejosat.936820 |
| IEEE | ERDEM E,Aydın T "A CNN-based hybrid model to detect Coronavirus disease." , ss.66 - 73, 2021. 10.31590/ejosat.936820 |
| ISNAD | ERDEM, EBRU - Aydın, Tolga. "A CNN-based hybrid model to detect Coronavirus disease". (2021), 66-73. https://doi.org/10.31590/ejosat.936820 |
| APA | ERDEM E, Aydın T (2021). A CNN-based hybrid model to detect Coronavirus disease. Avrupa Bilim ve Teknoloji Dergisi, 0(27), 66 - 73. 10.31590/ejosat.936820 |
| Chicago | ERDEM EBRU,Aydın Tolga A CNN-based hybrid model to detect Coronavirus disease. Avrupa Bilim ve Teknoloji Dergisi 0, no.27 (2021): 66 - 73. 10.31590/ejosat.936820 |
| MLA | ERDEM EBRU,Aydın Tolga A CNN-based hybrid model to detect Coronavirus disease. Avrupa Bilim ve Teknoloji Dergisi, vol.0, no.27, 2021, ss.66 - 73. 10.31590/ejosat.936820 |
| AMA | ERDEM E,Aydın T A CNN-based hybrid model to detect Coronavirus disease. Avrupa Bilim ve Teknoloji Dergisi. 2021; 0(27): 66 - 73. 10.31590/ejosat.936820 |
| Vancouver | ERDEM E,Aydın T A CNN-based hybrid model to detect Coronavirus disease. Avrupa Bilim ve Teknoloji Dergisi. 2021; 0(27): 66 - 73. 10.31590/ejosat.936820 |
| IEEE | ERDEM E,Aydın T "A CNN-based hybrid model to detect Coronavirus disease." Avrupa Bilim ve Teknoloji Dergisi, 0, ss.66 - 73, 2021. 10.31590/ejosat.936820 |
| ISNAD | ERDEM, EBRU - Aydın, Tolga. "A CNN-based hybrid model to detect Coronavirus disease". Avrupa Bilim ve Teknoloji Dergisi 27 (2021), 66-73. https://doi.org/10.31590/ejosat.936820 |
