Derin öğrenme mimarilerini kullanarak göğüs BT görüntülerinden otomatik Covid-19 tahmini

Year 2022, , 76 - 88, 30.09.2022

Veysel Türk , Hatice Çatal Reis , Serhat Kaya

https://doi.org/10.17714/gumusfenbil.1002738

Cited By: 2

Abstract

Makine öğrenmesi, son yıllarda hastalık tespiti ve segmentasyon araştırmalarında aktif olarak kullanılmaktadır. Son yıllarda insanlık, Koronavirus hastalığı 2019 (Covid-19) ile mücadele etmektedir. Göğüs-bilgisayarlı tomografi (BT) görüntüsü, olası Covid-19 hastalarını tespit etme de önemli bir araçtır. Bu çalışma, Derin Öğrenme (DÖ) algoritmaları kullanarak Covid-19 ve Covid-19 olmayan göğüs BT görüntülerini, sınıflandırmayı ve dört mimari kullanarak farklı parametrelerde başarılı sonuçlar elde edip edemeyeceğimizi araştırmayı amaçlamaktadır. Çalışma, kanıtlanmış pozitif Covid-19 CT görüntüleri üzerinde gerçekleştirildi ve görüntüler GitHub kamu platformundan elde edilmiştir. VGG16, VGG19, LeNet-5 ve MobileNet gibi dört farklı derin öğrenme mimarisi değerlendirildi. Performans değerlendirmelerinde ROC eğrisi, duyarlılık, doğruluk, F1-ölçütü, kesinlik ve RMSE kullanılmıştır. MobileNet modeli en iyi sonucu vermiştir sırasıyla; F1-ölçütü %95, doğruluk %95, kesinlik %100, duyarlılık %90, AUC %95 ve RMSE 0.23'tür. En düşük performansı ise; F1-ölçütü %90, doğruluk %89, kesinlik %90, duyarlılık%90, AUC %89 ve RMSE 0.32 ile VGG19 modeli vermiştir. Algoritmaların performansları karşılaştırıldığında en yüksek doğruluk sırasıyla MobileNet, LeNet-5, VGG16 ve VGG19'dan elde edilmiştir. Bu çalışma önerilen modeller çerçevesinde, Covid-19'u tespit etmek için derin öğrenme modellerinin kullanışlılığını göstermiştir. Bu nedenle araştırma, Covid-19 tespit çalışmalarında Tıp ve Mühendislik literatürüne katkı sağlayabilir.

Keywords

Covid-19, Derin öğrenme, LeNet-5, MobileNet, VGG16, VGG19

Automatic prediction of covid-19 from chest- computed tomography (CT) images using deep learning architectures

Abstract

Machine learning has been actively used in disease detection and segmentation in recent years. For the last few years, the world has been coping with the Coronavirus disease 2019 (COVID-19) pandemic. Chest-computerized tomography (CT) is often a meaningful way to detect and detect patients with possible COVID-19. This study aims to classify COVID-19 and non-COVID-19 chest-CT images using deep learning (DL) algorithms and investigate whether we can achieve successful results in different parameters using four architectures. The study was performed on proved positive COVID-19 CT images, and the datasets were obtained from the GitHub public platform. The study evaluated four different deep learning architectures of VGG16, VGG19, LeNet-5, and MobileNet. The performance evaluations were used with ROC curve, recall, accuracy, F1-score, precision, and Root Mean Square Error (RMSE). MobileNet model showed the best result; F1 score of 95%, the accuracy of 95%, the precision of 100%, recall of 90%, AUC of 95%, and RMSE of 0.23. On the other hand, VGG 19 model gave the lowest performance; F1 score of 90%, the accuracy of 89%, the precision of 90%, recall of 90%, AUC of 89%, and RMSE of 0.32. When the algorithms' performances were compared, the highest accuracy was obtained from MobileNet, LeNet-5, VGG16, and VGG19, respectively.
This study has proven the usefulness of deep learning models to detect COVID-19 in chest-CT images based on the proposed model framework. Therefore, it can contribute to the literature in Medical and Engineering in COVID-19 detection research.

Keywords

COVID-19, Deep learning, LeNet-5, MobileNet, VGG16, VGG19

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