Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Artificial Intelligence Based Video Processing Methods for Predicting COVID-19: Observational Study
COVID-19'u Tahmin Etmek için Yapay Zekâ Tabanlı Video İşleme Yöntemleri: Gözlemsel Çalışma
Burak YAĞINa, Emek GÜLDOĞANa, Cemil ÇOLAKa
aDepartment of Biostatistics and Medical Informatics, İnönü University Faculty of Medicine, Malatya, Türkiye
Turkiye Klinikleri J Biostat. 2022;14(1):22-31
doi: 10.5336/biostatic.2022-88805
Article Language: EN
Full Text
ABSTRACT
 Objective: The aim of this study is to develop a high-performance model and web-based clinical decision making method to successfully distinguish and classify COVID19 from bacterial pneumonia, viral pneumonia and healthy controls with lung ultrasound (LUS) videos using appropriate video processing techniques and artificial intelligence (AI) methods development of the support system. Material and Methods: In this study, the open source LUS video dataset at https://github.com/jannisborn/covid19_ultrasound was used. The dataset includes 32 healthy controls, 24 COVID-19, 24 bacterial pneumonia and 12 viral pneumonia class videos. In the video processing stage, 300 image frames were taken from the videos in each class. The images were divided into 80% (960) training and 20% (240) test datasets. In the modeling phase, the convolutional neural network (CNN) method, one of the deep neural network architectures in the keras library, was used. Accuracy, sensitivity, specificity, precision, Matthews' correlation coefficient and F1 score criteria are given to evaluate the performance of the model. A web-based system has been developed that can successfully detect COVID-19 using the, with the help of the AI-based model, Python Flask Library. Results: The accuracy in the test dataset was calculated as 93.39% for healthy control, COVID-19 and viral pneumonia, and 95.07% for bacterial pneumonia. Conclusion: According to the performance criteria values obtained with the video processing-based CNN model, it can be said that the developed system gives very successful predictions in the diagnosis of COVID-19, bacterial pneumonia and viral pneumonia.

Keywords: COVID-19; deep learning; video processing; image processing; convolutional neural networks
ÖZET
 Amaç: Bu çalışmanın amacı, uygun video işleme teknikleri ve yapay zekâ yöntemleri kullanılarak akciğer ultrason videoları ile COVID19'u bakteriyel pnömoni, viral pnömoni ve sağlıklı kontrollerden başarılı bir şekilde ayırt ederek sınıflandırmak için yüksek performansa sahip bir modelin ve web tabanlı klinik karar destek sisteminin geliştirilmesidir. Gereç ve Yöntemler: Bu çalışmada, https://github.com/jannisborn/covid19_ultrasound adresindeki açık kaynaklı akciğer ultrason video veri seti kullanılmıştır. Veri setinde bulunan videoların 32'si sağlıklı kontrol, 24'ü COVID-19, 24'ü bakteriyel pnömoni ve 12'si viral pnömoni şeklinde klinik olarak sınıflandırılmıştır. Video işleme aşamasında, her bir sınıftaki videolardan 300 görüntü karesi alınmıştır. Görüntülerin %80'i (960) eğitim ve %20'si (240) test veri seti olarak bölünmüştür. Modelleme aşamasında, keras kütüphanesinde bulunan derin sinir ağları mimarilerinden evrişimli sinir ağları CNN yöntemi kullanılmıştır. Oluşturulan modelin performansını değerlendirmek için doğruluk, duyarlılık, seçicilik, kesinlik, Matthews'in korelasyon katsayısı ve F1 skoru ölçütleri verilmiştir. Bunlara ek olarak oluşturulan yapay zekâ tabanlı model ile, Python Flask Kütüphanesi kullanılarak COVID-19'U başarılı bir şekilde tespit edebilen web tabanlı bir sistem geliştirilmiştir. Bulgular: Test veri setinde doğruluk sağlıklı kontrol, COVID-19 ve viral pnömoni için %93,39 ve bakteriyel pnömoni için ise %95,07 olarak hesaplanmıştır. Sonuç: Önerilen video işleme tabanlı CNN modeli ile elde edilen performans ölçütlerine göre geliştirilen sistemin COVID-19, bakteriyel pnömoni ve viral pnömoni tanısında oldukça başarılı tahminler verdiği ve klinik karar destek amacıyla kullanılabileceği söylenebilir.

Anahtar Kelimeler: COVID-19; derin öğrenme; video işleme; görüntü işleme; evrişimli sinir ağları
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