Koronavirüs hastalığını tespit etmek için CNN tabanlı bir hibrit model

Year 2021, Issue: 27, 66 - 73, 30.11.2021

Ebru Erdem , Tolga Aydin

https://doi.org/10.31590/ejosat.936820

Cited By: 1

Abstract

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.

Keywords

Covid-19, hibrit modeller, derin öğrenme, CNN

A CNN-based hybrid model to detect Coronavirus disease

Abstract

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.

Keywords

Covid-19, hybrid models, deep learning, CNN

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