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Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning

Year 2023, Volume: 15 Issue: 3, 49 - 63, 31.12.2023

Gözde Kahraman Zafer Civelek

https://doi.org/10.29137/umagd.1159663

Abstract

Abstract
The new virus disease (COVID-19) first came to China towards the end of December 2019 and became a pandemic all over the world. The disease caused a large number of people to be infected and die. Rapid diagnosis of the disease is of great importance in controlling transmission. A computed Tomography device provides successful results in the diagnosis of COVID-19 disease. In this study, two-class (COVID-19 and normal) data sets were created from 7200 lung Computed Tomography images diagnosed between March 2020 and November 2020 in a private hospital with the help of specialist physicians. Verification and testing processes were carried out on Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) algorithms from Machine Learning algorithms, and ResNet-50, DenseNet-201, InceptionResNetV2, Inceptionv3, VGG-16, Xception architectures from Deep Learning models. As a result of the studies, the DenseNet-201 architecture obtained the highest result from deep learning models with %99,35 training and test %98,75 accuracy rates, respectively. ANN %97,6, KNN %97,4 and SVM %96,9 accuracy rates were obtained from machine learning.

Keywords

machine learning deep learning cnn

References

  • Yang. X., Yu. Y, Xu. J, Shu. H, Xia. J, Liu. H, Wu. Y, Zhang. L, Yu. Z, Fang. M, Yu. T, Wang. Y, Pan. S, Zou. X, Yuan. S, Shang. Y. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine, (2020), 8(5), 475–481.
  • Brunese. L, Mercaldo. F, Reginelli. A, Santone. A. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Computer Methods and Programs in Biomedicine, (2020),196, 105608.
  • Wang. L, Lin. Z, Q, Wong. A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports, (2020) 10(1), 1–12.
  • M. DEMİRBİLEK, “Tam ve Kısmi Kapanma Stratejilerinin COVID-19 Salgını Üzerinden Karşılaştırılması.” vol. 2021, no. 2, pp. 1024–1034, 2021.
  • “Novel coronavirus –2019”. World health organization. https://covid19.who.int/accessed 15 October 2021.
  • Boeckmans. J, Cartuyvels. R, Hilkens. P, Bruckers. L, Magerman. K, Waumans. L, Raymaekers. M. Follow-up testing of borderline SARS-CoV-2 patients by rRT-PCR allows early diagnosis of COVID-19. Diagnostic Microbiology Infectious Disease, Journal Pre-proof, (2021),100(2), 115350.
  • Li. Y, Xia. L.Coronavirus disease. (COVID-19): Role of chest CT in diagnosis and management. American Journal of Roentgenology, (2019),214(6), 1280–1286.]
  • Jain. G, Mittal. D, Thakur. D, Mittal. M, K. A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Science Direct,(2020) 40(4), 1391–1405.
  • Wang. S, Zha. Y, Li. W, Wu. Q, Li. X, Niu. M., Wang. M., Qiu. X, Li. H., Yu. H, Gong. W, Bai. Y, Li. L, Zhu. Y, Wang. L, Tian, J. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. European Respiratory Journal, 2020 56(2), 2000775.
  • Gilanie. G, Bajwa. U, I. Waraich. M, M, Asghar. M, Kousar. R, Kashif. A, Aslam. R, S, Qasim.M, M, Rafique. H. Coronavirus (COVID-19) Detection from Chest Radiology Images using Convolutional Neural Networks.Biomedical Signal Processing and Control, (2021),66, 02490.
  • Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, and S. Singh. Deep Transfer Learning Based Classification Model for COVID-19 Disease,” Irbm, vol. 1,(2020), pp. 1–6.
  • Zhou. T, Lu. H, Yang. Z, Qiu. S, Huo. B, Dong. Y. The ensemble deep learning model for novel COVID-19 on CT images. Applied Soft Computing,(2021), 98, 106885.
  • Turkoglu, M. COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network.IRBM,(2021),pp. 1–8.
  • Huang. L, Han. R, Ai. T, Yu. P, Kang. H, Tao. Q, Xia. L. Serial Quantitative Chest CT Assessment of COVID-19 :A Deep Learning Apporach. Radiology: Cardiothoracic İmaging,(2020) 2(2) ,200075.
  • Gozes. O, Frid. M, Greenspan. H, Patrick. D. Title : Rapid AI Development Cycle for the Coronavirus ( COVID-19 ) Pandemic : Initial Results for Automated Detection Patient Monitoring using Deep Learning CT Image Analysis Article Type : Authors : Summary Statement : Key Results : List of abbreviati(2020).
  • Javor. D, Kaplan. H, Kaplan. A, Puchner. S, B, Krestan. C, Baltzer. P. Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography. European Journal of Radiology, (2020),133, 109402.
  • Y. Song et al. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. IEEE/ACM Trans. Comput. Biol. Bioinforma.,(2021), vol. 14, no. 8, 2021.
  • Abiodun. O, I. Jantan, A, Omolara. A, E. Dada, K, V. Mohamed, N, A. Arshad, H. State of the art in artificial neural network applications. Heliyon,(2018), 4(11), e00938. E. Sivari, Z. Civelek, and G. Kahraman, “Artificial neural network model estimating the initial dose of propofol used in general anesthesia,” El-Cezeri J. Sci. Eng., vol. 7, no. 3, pp. 1482–1495, 2020, doi: 10.31202/ecjse.764719.
  • A. Etyemez, “Yapay Sinir Ağları Yöntemi ile Optimum Takım Seçimi,” El-Cezeri Fen ve Mühendislik Derg., vol. 2019, no. 2, pp. 323–332, 2019, doi: 10.31202/ecjse.511882. Dongare. A, D. Kharde, R. R, Kachare. A, D. Introduction to Artificial Neural Network (ANN) Methods. International Journal of Engineering and Innovative Technology (IJEIT),(2012), 2(1), 189–194.
  • Hubel. D, H, Wiesel. T, N. And functıonal archıteture in the cat's vısual cortex from the neurophysiolojy laboratory, Department of Pharmacology central nervous system is the great diversity of its cell types and inter- receptive fields of a more complex type (Part I), (1962),106–154.
  • A. ORMAN, U. KÖSE, and T. YİĞİT, “Açıklanabilir Evrişimsel Sinir Ağları ile Beyin Tümörü Tespiti,” El-Cezeri Fen ve Mühendislik Derg., vol. 2021, no. 3, pp. 1323–1337, 2021, doi: 10.31202/ecjse.924446.
  • M. C. İBAN and E. ŞENTÜRK, “İyonosfer Parametrelerinin Çok Katmanlı Algılayıcılar ile Kestirimi,” El-Cezeri Fen ve Mühendislik Derg., vol. 2021, no. 3, pp. 1480–1494, 2021, doi: 10.31202/ecjse.948557.
  • Liu. T, Fang. S, Zhao. Y, Wang. P, Zhang. J. Implementation of Training Convolutional Neural Networks. Arxiv, (2015),1506, 01195.
  • Qian. S, Liu. H, Liu. C, Wu. S, Wong. H,S. Adaptive activation functions in convolutional neural networks. Neurocomputing, (2018),272, 204–212. Wei. L, Cai. J, Nguyen. V, Chu. J, Wen. K. P-SFA: Probability based Sigmoid Function Approximation for Low-complexity Hardware Implementation. Microprocessors and Microsystems,(2020), 76, 103105.
  • Shaban. W, M. Rabie, A. H, Saleh. A, I, & Abo-Elsoud, M. A. A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems,(2020), 205, 106270.
  • D. KAYA, M. TÜRK, and T. KAYA, “Examining the Effect of Dimension Reduction on EEG Signals by K-Nearest Neighbors Algorithm,” El-Cezeri Fen ve Mühendislik Derg., vol. 5, no. 2, pp. 591–595, 2018, doi: 10.31202/ecjse.385192.
  • Dixit. A, Mani. A, Bansal. R. CoV2-Detect-Net: Design of COVID-19 prediction model based on hybrid DE-PSO with SVM using Chest X-ray images. Information Sciences, (2021),676-692.
  • A.Jaiswal, N. Gianchandani, D. Singh and V. Kumar.Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. J. Biomol. Struct. Dyn, (2020), vol. 0, no. 0, pp. 1–8.
  • S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. Jamalipour Soufi.Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.Med. Image Anal,(2020), vol. 65.

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

Year 2023, Volume: 15 Issue: 3, 49 - 63, 31.12.2023

Gözde Kahraman Zafer Civelek

https://doi.org/10.29137/umagd.1159663

Abstract

Yeni koronavirüs hastalığı (COVID-19) ilk olarak Aralık 2019'un sonlarına doğru Çin'de ortaya çıkarak tüm dünyada bir pandemi haline geldi. Hastalık çok sayıda insanın enfekte olmasına ve ölmesine neden oldu. Hastalığın hızlı teşhis edilmesi bulaşmanın control edilmesinde büyük önem taşımaktadır. Bilgisayarlı Tomografi cihazı, COVID-19 hastalığının teşhisinde başarılı sonuçlar elde etmektedir. Bu çalışmada, özel bir hastanenin Mart 2020 – Kasım 2020 tarihleri arasında teşhis edilmiş 7200 akciğer Bilgisayarlı Tomografi görüntüsü uzman hekimler yardımıyla iki sınıfla ayrılarak (COVID-19 ve normal) veri setleri oluşturulmuştur. Yapay Sinir Ağı (YSA), Destek Vektör Makinesi (SVM), Makine Öğrenmesi algoritmalarından K-En Yakın Komşuluk (KNN) algoritmaları ve ResNet-50, DenseNet-201, InceptionResNetV2, Inceptionv3, VGG-16, Xception Derin Öğrenme modelleri üzerinde doğrulama ve test işlemleri gerçekleştirilmiştir. Gerçekleştirilen çalışmalar sonucunda DenseNet-201 mimarisi, sırasıyla %99,35 eğitim ve test %98,75 doğruluk oranları ile derin öğrenme modellerinden en yüksek sonucu almıştır. Makine öğrenmesinden ANN %97,6, KNN %97,4 ve SVM %96,9 doğruluk oranları elde edilmiştir.

Keywords

derin öğrenme makine öğrenmesi cnn

Thanks

Çankırı Özel Karatekin Hastanesi Müdürü Sayın Önder İZMİRLİOĞLU’na, Radyoloji Bölümü Uzman Doktoru Sayın Recep ER’e, Radyoloji Teknikeri Sayın Erkan KULOĞLU’na ve tüm Radyoloji bölümü çalışanlarına teşekkürlerimizi ve saygılarımızı sunarız.

References

  • Yang. X., Yu. Y, Xu. J, Shu. H, Xia. J, Liu. H, Wu. Y, Zhang. L, Yu. Z, Fang. M, Yu. T, Wang. Y, Pan. S, Zou. X, Yuan. S, Shang. Y. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine, (2020), 8(5), 475–481.
  • Brunese. L, Mercaldo. F, Reginelli. A, Santone. A. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Computer Methods and Programs in Biomedicine, (2020),196, 105608.
  • Wang. L, Lin. Z, Q, Wong. A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports, (2020) 10(1), 1–12.
  • M. DEMİRBİLEK, “Tam ve Kısmi Kapanma Stratejilerinin COVID-19 Salgını Üzerinden Karşılaştırılması.” vol. 2021, no. 2, pp. 1024–1034, 2021.
  • “Novel coronavirus –2019”. World health organization. https://covid19.who.int/accessed 15 October 2021.
  • Boeckmans. J, Cartuyvels. R, Hilkens. P, Bruckers. L, Magerman. K, Waumans. L, Raymaekers. M. Follow-up testing of borderline SARS-CoV-2 patients by rRT-PCR allows early diagnosis of COVID-19. Diagnostic Microbiology Infectious Disease, Journal Pre-proof, (2021),100(2), 115350.
  • Li. Y, Xia. L.Coronavirus disease. (COVID-19): Role of chest CT in diagnosis and management. American Journal of Roentgenology, (2019),214(6), 1280–1286.]
  • Jain. G, Mittal. D, Thakur. D, Mittal. M, K. A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Science Direct,(2020) 40(4), 1391–1405.
  • Wang. S, Zha. Y, Li. W, Wu. Q, Li. X, Niu. M., Wang. M., Qiu. X, Li. H., Yu. H, Gong. W, Bai. Y, Li. L, Zhu. Y, Wang. L, Tian, J. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. European Respiratory Journal, 2020 56(2), 2000775.
  • Gilanie. G, Bajwa. U, I. Waraich. M, M, Asghar. M, Kousar. R, Kashif. A, Aslam. R, S, Qasim.M, M, Rafique. H. Coronavirus (COVID-19) Detection from Chest Radiology Images using Convolutional Neural Networks.Biomedical Signal Processing and Control, (2021),66, 02490.
  • Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, and S. Singh. Deep Transfer Learning Based Classification Model for COVID-19 Disease,” Irbm, vol. 1,(2020), pp. 1–6.
  • Zhou. T, Lu. H, Yang. Z, Qiu. S, Huo. B, Dong. Y. The ensemble deep learning model for novel COVID-19 on CT images. Applied Soft Computing,(2021), 98, 106885.
  • Turkoglu, M. COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network.IRBM,(2021),pp. 1–8.
  • Huang. L, Han. R, Ai. T, Yu. P, Kang. H, Tao. Q, Xia. L. Serial Quantitative Chest CT Assessment of COVID-19 :A Deep Learning Apporach. Radiology: Cardiothoracic İmaging,(2020) 2(2) ,200075.
  • Gozes. O, Frid. M, Greenspan. H, Patrick. D. Title : Rapid AI Development Cycle for the Coronavirus ( COVID-19 ) Pandemic : Initial Results for Automated Detection Patient Monitoring using Deep Learning CT Image Analysis Article Type : Authors : Summary Statement : Key Results : List of abbreviati(2020).
  • Javor. D, Kaplan. H, Kaplan. A, Puchner. S, B, Krestan. C, Baltzer. P. Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography. European Journal of Radiology, (2020),133, 109402.
  • Y. Song et al. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. IEEE/ACM Trans. Comput. Biol. Bioinforma.,(2021), vol. 14, no. 8, 2021.
  • Abiodun. O, I. Jantan, A, Omolara. A, E. Dada, K, V. Mohamed, N, A. Arshad, H. State of the art in artificial neural network applications. Heliyon,(2018), 4(11), e00938. E. Sivari, Z. Civelek, and G. Kahraman, “Artificial neural network model estimating the initial dose of propofol used in general anesthesia,” El-Cezeri J. Sci. Eng., vol. 7, no. 3, pp. 1482–1495, 2020, doi: 10.31202/ecjse.764719.
  • A. Etyemez, “Yapay Sinir Ağları Yöntemi ile Optimum Takım Seçimi,” El-Cezeri Fen ve Mühendislik Derg., vol. 2019, no. 2, pp. 323–332, 2019, doi: 10.31202/ecjse.511882. Dongare. A, D. Kharde, R. R, Kachare. A, D. Introduction to Artificial Neural Network (ANN) Methods. International Journal of Engineering and Innovative Technology (IJEIT),(2012), 2(1), 189–194.
  • Hubel. D, H, Wiesel. T, N. And functıonal archıteture in the cat's vısual cortex from the neurophysiolojy laboratory, Department of Pharmacology central nervous system is the great diversity of its cell types and inter- receptive fields of a more complex type (Part I), (1962),106–154.
  • A. ORMAN, U. KÖSE, and T. YİĞİT, “Açıklanabilir Evrişimsel Sinir Ağları ile Beyin Tümörü Tespiti,” El-Cezeri Fen ve Mühendislik Derg., vol. 2021, no. 3, pp. 1323–1337, 2021, doi: 10.31202/ecjse.924446.
  • M. C. İBAN and E. ŞENTÜRK, “İyonosfer Parametrelerinin Çok Katmanlı Algılayıcılar ile Kestirimi,” El-Cezeri Fen ve Mühendislik Derg., vol. 2021, no. 3, pp. 1480–1494, 2021, doi: 10.31202/ecjse.948557.
  • Liu. T, Fang. S, Zhao. Y, Wang. P, Zhang. J. Implementation of Training Convolutional Neural Networks. Arxiv, (2015),1506, 01195.
  • Qian. S, Liu. H, Liu. C, Wu. S, Wong. H,S. Adaptive activation functions in convolutional neural networks. Neurocomputing, (2018),272, 204–212. Wei. L, Cai. J, Nguyen. V, Chu. J, Wen. K. P-SFA: Probability based Sigmoid Function Approximation for Low-complexity Hardware Implementation. Microprocessors and Microsystems,(2020), 76, 103105.
  • Shaban. W, M. Rabie, A. H, Saleh. A, I, & Abo-Elsoud, M. A. A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems,(2020), 205, 106270.
  • D. KAYA, M. TÜRK, and T. KAYA, “Examining the Effect of Dimension Reduction on EEG Signals by K-Nearest Neighbors Algorithm,” El-Cezeri Fen ve Mühendislik Derg., vol. 5, no. 2, pp. 591–595, 2018, doi: 10.31202/ecjse.385192.
  • Dixit. A, Mani. A, Bansal. R. CoV2-Detect-Net: Design of COVID-19 prediction model based on hybrid DE-PSO with SVM using Chest X-ray images. Information Sciences, (2021),676-692.
  • A.Jaiswal, N. Gianchandani, D. Singh and V. Kumar.Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. J. Biomol. Struct. Dyn, (2020), vol. 0, no. 0, pp. 1–8.
  • S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. Jamalipour Soufi.Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.Med. Image Anal,(2020), vol. 65.
There are 29 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Articles
Authors

Gözde Kahraman 0000-0001-6478-5466

Zafer Civelek 0000-0001-6838-3149

Publication Date December 31, 2023
Submission Date August 9, 2022
Published in Issue Year 2023 Volume: 15 Issue: 3

Cite

APA Kahraman, G., & Civelek, Z. (2023). Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning. International Journal of Engineering Research and Development, 15(3), 49-63. https://doi.org/10.29137/umagd.1159663

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