Diagnosis and Detection of COVID-19 from Lung Tomography Images Using Deep Learning and Machine Learning Methods



Deep Learning, Machine Learning, CNN, DNN, KNN, COVID-19, Tomography, Artificial Intelligence


Coronavirus (COVID-19) is an epidemic disease that spreads all over the world in a very short time and has fatal consequences. Such infectious diseases must be correctly detected without harming people or with minimal harm, and the necessary treatment must be initiated early. At this point, traditional treatment methods applied by doctors may be insufficient or diagnosis and treatment may be delayed. Therefore, Artificial Intelligence (AI) and Machine Learning (ML) techniques that are widely used in many areas and effective in solving complex problems come to the fore, to obtain a more effective and successful treatment in these situations. This study aimed to diagnose and detect the COVID-19 image from different lung tomography images (COVID-19, viral pneumonia, bacterial pneumonia, and normal) with AI and ML techniques. In this context, it was used the K-Nearest Neighbor (KNN) method, which is an ML algorithm, and Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) deep learning approaches which are among the current techniques of AI. In addition, the results were tested by creating models with combinations of different optimization and activation functions and neuron numbers in the CNN method. Thus, the application potential of CNN, DNN, and KNN methods in image recognition and classification were seen and the success of the proposed model was demonstrated with the obtained findings.


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Ozturk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yildirim, O., & Acharya, U. “Automated detection of COVID-19 cases using deep neural networks with X-ray images”. Computers in Biology and Medicine,1-11, 2020.

Chen, Y. T., de Gracia, M. M., Díez-Tascón, A., Alonso-González, R., Rodríguez-Fuertes, P., Parra-Gordo, M. L., & Fuertes, R. L. “Correlation between Chest Computed Tomography and Lung Ultrasonography in Patients with Coronavirus Disease 2019 (COVID-19)”. Ultrasound in Medicine & Biology, 2020.

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.

Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla Jr, C. N., & Costa, Y. M. “COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios”. Computer Methods and Programs in Biomedicine, 2020.

Togacar, M., Ergen, B., & Co1mert, Z., “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches”. Computers in Biology and Medicine, 1-12, 2020.

Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., & Singh, V. “Application of Deep Learning for Fast Detection of COVID-19 in X-Rays using nCOVnet”. Chaos, Solitons & Fractals, 2020.

Apostolopoulos, I., Sokratis, A., & Mpesiana, T., “Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases”. Journal of Medical and Biological Engineering, 462-469, 2020.

Mahmud, T., Rahman, M. A., & Fattah, S. A., “CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization”, Computers in biology and medicine, 2020.

Khan, A. I., Shah, J. L., & Bhat, M. M.. “Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images”, Computer Methods and Programs in Biomedicine, 2020.

Koklu, M., Kahramanli, H., & Allahverdi, N. (2012). A new approach to classification rule extraction problem by the real value coding. International Journal of Innovative Computing, Information and Control, 8(9), 6303-6315.

Hassantabar, S., Ahmadi, M., & Sharifi, A. (2020). Diagnosis and Detection of Infected Tissue of COVID-19 Patients Based on Lung X-Ray Image Using Convolutional Neural Network Approaches. Chaos, Solitons & Fractals, 140, 110170.

Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Ashqar, B. A. “COVID-19 Detection using Artificial Intelligence”, International Journal of Academic Engineering Research, 18-25, 2020.

Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M., “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning”, Journal of Biomolecular Structure and Dynamics, 1-9, 2020.

Rahimzadeh, M., Attar, A., & Sakhaei, S. M. (2020). “A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset”, Retrieved from The preprint server for health sciences: https://www.medrxiv.org/content/10.1101/2020.06.08.20121541v2, (4 Temmuz, 2020).

Nour, M., Comert, Z., & Polat, K., “A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization”, Applied Soft Computing Journal, 1-13, 2020.

Pathak, Y., Shukla, P., Tiwari, A., Stalin, S., Singh, S., & Shukla, P., “Deep Transfer Learning Based Classification Model for COVID-19, Disease. Innovation and Research in BioMedical Engineering, 1-6, 2020.

Rahimzadeh, M., & Attar, A. (2020). A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 1-9.

Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., & Cao, K. (2020). Artificial intelligence distinguishes COVID- 19 from community-acquired pneumonia on chest CT. Radiology.

Sen, S., Saha, S., Chatterjee, S., Mirjalili, S., & Sarkar, R. (2021). A bi-stage feature selection approach for COVID-19 prediction using chest CT images. Applied Intelligence, 51 (12), 8985-9000.

Sinha, A., & Rathi, M. (2021). COVID-19 prediction using AI analytics for South Korea. Applied Intelligence, 51(12), 8579-8597.

Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2021). Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images. arXiv preprint arXiv:2102.06535.

Ozkan, I. & Ulker, E., “Deep Learning and Deep Learning Models Used in Image Analysis”, Gaziosmanpasa Journal of Scientific Research, 6(3), 85-104, 2017.

Seker, A., Diri, B., & Balik, H. H. “A Review about Deep Learning Methods and Applications”. Gazi Journal Of Engineering Sciences, 3(3), 47-64, 2017.

L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process. vol. 7, no. 3–4, pp. 197–387, 2014.

Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z., “Deep learning for health informatics”. IEEE Journal of biomedical and health informatics, 21(1), 4-21, 2016.

Schmidhuber, J. Deep learning in neural networks: An overview. CoRR abs/1404.7828 (2014).

Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2019). “Performance comparison of carotid artery intima media thickness classification by deep learning methods”, In HORA 2019: International Congress on Human-Computer Interaction, Optimization and Robotic Applications, 4(5): 125-131.

Karahan, S., & Akgul, Y. S. (2016, May). Eye detection by using deep learning. In 2016 24th Signal Processing and Communication Application Conference (SIU) (pp. 2145-2148). IEEE.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.s

Savaş, S., (2022). “Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures”, Arabian Journal for Science and Engineering, 47, 2201–2218. https://doi.org/10.1007/s13369-021-06131-3

Calp, M.H., (2021) Use of Deep Learning Approaches in Cancer Diagnosis. In: Kose U., Alzubi J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_15.

Kizrak, M. A., & Bolat, B. “A Comprehensive Survey of Deep Learning in Crowd Analysis”. The Journal of Information Technologies, 11(3), 263-286, 2018.

Dogan, F., & Turkoğlu, İ., “The Comparison Of Leaf Classification Performance Of Deep Learning Algorithms”, Sakarya University Journal of Computer and Information Sciences, 1(1), 10-21, 2018.

Amidi, S. “Afshine Amidi ve Shervine Amidi written by, Ayyuce Kizrak ve Yavuz Komecoğlu it is translated by. Evrişimli Sinir Ağları. Standford University: Access-Adress: https://stanford.edu/~shervine/l/tr/teaching/cs-230/cheatsheet-convolutional-neural-networks, , 2018.

Savaş, S. , Topaloğlu, N. , Kazcı, Ö. & Koşar, P. N. (2022). “Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet”, Bilişim Teknolojileri Dergisi, 15(1), 1-12.

Chollet F, Allaire JJ. Deep Learning with R: Manning Publications Co.; 2018.

Patterson J, Gibson A. Deep Learning: A Practitioner's Approach. Beijing: O'Reilly; 2017.

Fernandez, C., Soria, E., Martin, J.D., & Serrano, A.J. (2006). Neural networks for animal science applications: Two case studies. Expert Systems with Applications. 31, 444-450.

Priddy, K. L., & Keller, P. E. (2005). Artificial neural network: An Introduction, 1st. ed., Spie Press, Washington.

Microsoft Azure. (2020). Makine öğrenmesi nedir? Bulut Bilişim Hizmetleri I Microsoft Azure: Access Address: https://azure.microsoft.com/tr-tr/overview/what-is-machine-learning-platform/

Atalay, M., Celik, E. "Artificial Intelligence and Machine Learning Applications in Big Data Analysis." Mehmet Akif Ersoy University Journal of Social Sciences Institute. 9.22 (2017): 155-172.

Kilinc, D., Borandag, E., Yucalar, F., Tunali, V., Simsek, M., & Ozcift, A., “Classification of Scientific Articles Using Text Mining with KNN Algorithm and R Language”, Marmara Journal of Pure and Applied Sciences, 2016.

Calp, M.H., Butuner, R., Kose, U. et al. IoHT-based deep learning controlled robot vehicle for paralyzed patients of smart cities. J Supercomput (2022).

Ulgen, E. K. (2017). Makine Öğrenimi Bölüm-2 (k-En Yakın Komsuluk). Medium: Access Address: https://medium.com/@k.ulgen90/makine-%C3%B6%C4%9Frenimi-b%C3%B6l%C3%BCm-2-6d6d120a18e1 Access Date: 02.10.20.




How to Cite

R. Butuner and M. Calp, “Diagnosis and Detection of COVID-19 from Lung Tomography Images Using Deep Learning and Machine Learning Methods”, Int J Intell Syst Appl Eng, vol. 10, no. 2, pp. 190–200, May 2022.



Research Article