Boosting Performance of Transfer Learning Model for Diagnosis of COVID-19 from Computer Tomography Scans

Year 2021, , 35 - 45, 27.05.2021

Onur Karaman

https://doi.org/10.29233/sdufeffd.830351

Cited By: 3

Abstract

Early-stage rapid and accurate diagnosis of COVID-19 pneumonia is of great importance as a measure to the fight against the pandemic. Even if real-time reverse transcription-polymerase chain reaction (RT-PCR) test seems like a gold standard for determining COVID-19, the availability and the accuracy is still a challenge. Thus, alternative diagnostic techniques are required for controlling the spreading of the disease. Amongst the radiodiagnostic methods, the computer tomography (CT) technique is one of the most powerful candidates for screening COVID-19 pneumonia accurately. In this study, it is aimed to develop a reliable transfer learning-based CNN model tailored to detect the COVID-19 from chest CT scans with high accuracy and sensitivity to help to accelerate the application of the required treatment and taking of measures. The CT scan dataset used in the training process of the CNN model was obtained from “SARS-CoV-2 CT-Scan Dataset”. This dataset contains 1252 CT scans for positive COVID-19 case and 1230 CT scans for the non-COVID-19 case, 2482 CT scans in total, all data have been collected from real patients from hospitals in Sao Paulo, Brazil. ResNet18, ResNet50, VGG16, AlexNet, and SqueezeNet1_1 architectures were re-trained to extract COVID-19 classes by transfer learning. The highest classification performance parameters were obtained for ResNet50 architecture and were calculated as 99.80% accuracy, 99.61 % precision, and 100.00% sensitivity. The activation maps were created to highlight the crucial areas of the CT images and improve causality and intelligibility. The developed transfer learning model can be utilized for reliable clinical diagnosis of COVID-19 cases from CT images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

Keywords

Corona virus (COVID-19), Deep learning, Convolutional neural network (CNN), Transfer learning, Computer tomography

Thanks

The author would like to thank the anonymous referees for helpful suggestions that improved the paper.

Bilgisayarlı Tomografi Görüntülerinden COVID-19 Teşhisi İçin Geliştirilen Transfer Öğrenim Modelinin Performansının Artırılması

Abstract

COVID-19 pnömonisinin erken aşamada hızlı ve doğru teşhisi, pandemiyle mücadelede bir önlem olarak büyük önem taşımaktadır. Gerçek zamanlı ters transkriptaz-polimeraz zincir reaksiyonu (RT-PCR) testi, COVID-19'u belirlemek için altın bir standart gibi görünse bile, kullanılabilirliği ve doğruluğu hala bir zorluktur. Bu nedenle, hastalığın yayılmasını kontrol etmek için alternatif teşhis teknikleri gereklidir. Radyodiyagnostik yöntemler arasında, bilgisayarlı tomografi (BT) tekniği COVID-19 pnömonisini doğru bir şekilde teşhis etmek için en güçlü adaylardan biridir. Bu çalışmada, gerekli tedavinin uygulanmasını ve önlemlerin alınmasını hızlandırmaya yardımcı olmak için göğüs BT taramalarından COVID-19'u yüksek doğruluk ve hassasiyetle tespit etmek üzere uyarlanmış, aktarımı öğrenmeye dayalı güvenilir bir CNN modelinin geliştirilmesi amaçlanmıştır. CNN modelinin eğitim sürecinde kullanılan CT görüntüleri veri seti “SARS-CoV-2 CT-Scan Veri Seti”nden elde edilmiştir. Bu veri kümesi, pozitif COVID-19 vakası için 1252 CT görüntüsü ve COVID-19 olmayan vaka için 1230 CT görüntüsü, toplamda 2482 CT görüntüsü içerir, tüm veriler Sao Paulo, Brezilya'daki hastanelerdeki gerçek hastalardan toplanmıştır. ResNet18, ResNet50, VGG16, AlexNet ve SqueezeNet1_1 mimarileri, transfer öğrenimi ile COVID-19 sınıflarını belirlemek için yeniden eğitildi. En yüksek sınıflandırma performans parametreleri ResNet50 mimarisi için elde edilmiş ve % 99,80 doğruluk,% 99,61 kesinlik ve% 100,00 duyarlılık olarak hesaplanmıştır. CT görüntülerinin önemli alanlarını vurgulamak ve nedensellik ve anlaşılırlığı geliştirmek için aktivasyon haritaları oluşturuldu. Geliştirilen transfer öğrenme modeli, triyajı hızlandırmak ve hastalık kontrolü için kritik zamandan tasarruf etmek ve radyoloğun ilk tanılarını doğrulamasına yardımcı olmak için CT görüntülerinden COVID-19 vakalarının güvenilir klinik teşhisinde kullanılabilir.

Keywords

Corona virüsü (COVID-19), Derin öğrenme, Evrişimsel sinir ağı, Transfer öğrenimi, Bilgisayarlı tomografi

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