Göğüs Röntgeni Görüntülerine Dayalı COVID-19'u Sınıflandırmak için Bilgisayar Destekli Bir Tanı Aracı

Abstract

Amaç: COVID-19 dünya çapında bir salgın olduğu için, evrişimli sinir ağı (CNN) kullanılarak COVID-19 tespiti olağanüstü bir araştırma tekniği olmuştur. Bildirilen çalışmalarda, çeşitli tıbbi görüntüler kullanılarak derin öğrenme yöntemlerine dayalı olarak COVID-19'u tahmin edebilen birçok model oluşturulmuş; ancak, klinik karar destek sistemleri sınırlı kalmıştır. Bu çalışmanın amacı, X- ışını görüntülerine dayalı başarılı bir derin öğrenme modeli ve COVID-19'un doğru tespiti için bilgisayar destekli, hızlı, ücretsiz ve web tabanlı bir tanı aracı geliştirmektir. Gereç ve Yöntem: Bu çalışmada, sınıflandırma açısından daha önce yayınlanmış birçok CNN modelinden daha iyi performans gösteren X-ışını görüntüleri kullanılarak COVID-19'u tespit etmek için 15 katmanlı bir CNN modeli kullanıldı. Model performansı Doğruluk, Matthews Korelasyon Katsayısı (MCC), F1 Skoru, Seçiçilik, Duyarlılık, Youden Endeksi, Kesinlik (Pozitif Tahmin Değeri: PPV), Negatif Tahmin Değeri (NPV) ve Karışıklık Matrisine (Sınıflandırma matrisi) dayalı olarak değerlendirildi. Çalışmanın ikinci aşamasında Python Flask kütüphanesi, JavaScript ve Html kodları kullanılarak COVID-19 için bilgisayar destekli tanı aracı geliştirildi. Bulgular: COVID-19 tanısına yönelik model, eğitim setinde ortalama %98.68 ve test setinde %96.98 doğruluk oranına sahiptir. Değerlendirme ölçütlerinden minimum değerler MCC ve Youden Endeksi için %93.4, maksimum değer ise duyarlılık ve NPV ölçütlerinde % 97.8 olarak elde edilmiştir. Daha yüksek bir duyarlılık değeri, daha düşük bir yanlış negatif (FN) değeri anlamına gelir ve düşük bir FN değeri, COVID-19 vakaları için cesaret verici bir sonuçtur. Bu sonuç çok önemlidir, çünkü gözden kaçan COVID-19 vakalarını (yanlış negatifler) en aza indirmek bu araştırmanın ana hedeflerinden biridir. Sonuç: COVID-19'un dünya çapında hızla yayıldığı bu dönemde, ücretsiz ve web tabanlı COVID-19 X-Ray klinik karar destek aracının oldukça etkili ve hızlı bir tanı aracı olabileceği düşünülmektedir. Bilgisayar destekli sistem, doktorlara ve radyologlara hastalık hakkında klinik kararlar vermede yardımcı olabileceği gibi, teşhis, takip ve prognoz konusunda da destek sağlayabilir. Geliştirilen bilgisayar destekli tanı aracına http://biostatapps.inonu.edu.tr/CSYX/ adresinden genel erişim sağlanabilmektedir.

Keywords

• Evrişimli sinir ağı
• COVID-19
• Görüntü işleme
• Derin öğrenme
• Bilgisayar destekli tanı sistemleri

A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images

Abstract

Objective: Since COVID-19 is a worldwide pandemic, COVID-19 detection using a convolutional neural network (CNN) has been an extraordinary research technique. In the reported studies, many models that can predict COVID-19 based on deep learning methods using various medical images have been created; however, clinical decision support systems have been limited. The aim of this study is to develop a successful deep learning model based on X-ray images and a computer-assisted, fast, free and web-based diagnostic tool for accurate detection of COVID-19. Method: In this study a 15-layer CNN model was used to detect COVID-19 using X-ray images, which outperformed many previously published CNN models in terms of classification. The model performance is evaluated according to Accuracy, Matthews Correlation Coefficient (MCC), F1 Score, Specificity, Sensitivity (Recall), Youden’s Index, Precision (Positive Predictive Value: PPV), Negative Predictive Value (NPV), and Confusion Matrix (Classification matrix). In the second phase of the study, the computer-aided diagnostic tool for COVID-19 disease was developed using Python Flask library, JavaScript and Html codes. Results: The model to diagnose COVID-19 has an average accuracy of 98.68 % in the training set and 96.98 % in the testing set. Among the evaluation metrics, the minimum value is 93.4 % for MCC and Youden’s index, and the maximum value is 97.8 for sensitivity and NPV. A higher sensitivity value means a lower false negative (FN) value, and a low FN value is an encouraging outcome for COVID-19 cases. This conclusion is crucial because minimizing the overlooked cases of COVID-19 (false negatives) is one of the main goals of this research. Conclusion: In this period when COVID-19 is spreading rapidly around the world, it is thought that the free and web-based COVID-19 X-Ray clinical decision support tool can be a very effective and fast diagnostic tool. The computer-aided system can assist physicians and radiologists in making clinical decisions about the disease, as well as provide support in diagnosis, follow-up, and prognosis. The developed computer-assisted diagnosis tool can be publicly accessed at http://biostatapps.inonu.edu.tr/CSYX/.

Keywords

• Convolutional Neural Network
• COVID-19
• Image Processing
• Deep Learning
• Computer-Aided Diagnostic Systems

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