Detection of COVID-19 Disease with Machine Learning Algorithms from CT Images

Year 2024, , 169 - 181, 01.03.2024

Mahmut Nedim Ekersular , Ahmet Alkan

https://doi.org/10.35378/gujs.1150388

Cited By: 5

Abstract

COVID-19, caused by the SARS-COV-2 virus, which has killed more than 6 million people, is one of the most contagious diseases in human history. It has seriously affected every area that people come into contact with, from business life to economy, from transportation to education, from social life to psychology. Although the developed vaccines provide a partial decrease in the number of deaths, the mutations that the virus constantly undergoes and the increase in the transmission rate accordingly reduce the effectiveness of the vaccines, and the number of deaths tends to increase as the number of infected people. It is undoubtedly important that the detection of this epidemic disease, which is the biggest crisis that humanity has experienced in the last century after World War II, is carried out accurately and quickly. In this study, a machine learning-based artificial intelligence method has been proposed for the detection of COVID-19 from computed tomography images. The features of images with two classes are extracted using the Local Binary Pattern. The images reserved for training in the dataset were used for training machine learning models. Trained models were tested with previously unused test images. While the Fine K-Nearest Neighbors model reached the highest accuracy with a value of 0.984 for the training images, the highest accuracy value was obtained by the Cubic Support Vector Machine with 0.93 for the test images. These results are higher than the deep learning-based study using the same data set.

Keywords

Classification, Machine learning, Local binary pattern, Computerized tomography, COVID-19

References

• [1] Sünnetci, K.M., Alkan, A., Tar, E., “Göğüs X-Ray görüntülerinin AlexNet tabanlı sınıflandırılması”, Journal of Computer Science, IDAP-2021(Special): 375-384, (2021).
• [2] https://covid19.saglik.gov.tr/TR-66300/covid-19-nedir-.html Access Date: 23.07.2022
• [3] https://www.worldometers.info/coronavirus/ Access Date: 23.07.2022
• [4] https://www.iaea.org/newscenter/news/how-is-the-covid-19-virus-detected-using-real-time-rt-pcr Access Date: 23.07.2022
• [5] https://www.cdc.gov/coronavirus/2019-ncov/science/science-and-research.html Access Date: 23.07.2022
• [6] https://en.wikipedia.org/wiki/CT_scan Access Date: 23.07.2022.
• [7] Kaya, B., Önal, M., “A CNN Based Method for Detecting Covid-19 from CT Images”, Journal of Computer Science, IDAP-2021(Special): 1-10, (2021).
• [8] Rasheed, J., Hameed, A. A., Djeddi, C., Jamil, A., Al-Turjman, F., “A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images”, Interdisciplinary sciences, computational life sciences, 13(1): 103–117, (2021).
• [9] Hussain, L., Nguyen, T., Li, H., Abbasi, A.A., Lone, K.J., Zhao, Z., Zaib, M., Chen, A., & Duong, T.Q., “Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection”, 19(88), (2020).
• [10] Zhang, Y., Su, L., Liu, Z., Tan, W., Jiang, Y., Cheng, C., “A semi-supervised learning approach for COVID-19 detection from chest CT scans”, Neurocomputing, 503: 314-324, (2022).
• [11] Baghdadi, N. A., Malki, A., Abdelaliem, S. F., Magdy Balaha, H., Badawy, M., Elhosseini, M., “An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based Convolutional Neural Network”, Computers in Biology and Medicine, 144, 105383, (2022).
• [12] https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset Access Date: 23.07.2022
• [13] Kızrak, A., “Local Binary Pattern Yöntemi ile Yüz İfadelerinin Tanınması”, Şekil Tanıma Proje Raporu, (2014).
• [14] Vaibhaw, Sarraf, J., Pattnaik, P. K., “Brain–computer interfaces and their applications”, An Industrial IoT Approach for Pharmaceutical Industry Growth, 2: 31–54, (2020).
• [15] https://www.geeksforgeeks.org/ensemble-classifier-data-mining/ Access Date: 23.07.2022
• [16] https://medium.com/@ekrem.hatipoglu/machine-learning-prediction-algorithms-decision-tree-random-forest-part-5-2970905c021e Access Date: 23.07.2022
• [17] Alkan, A., “Analysis of knee osteoarthritis by using fuzzy c-means clustering and SVM classification”, Scientific Research and Essays 6 (20): 4213-4219, (2011).
• [18] Özbeyaz, A., “EEG-Based classification of branded and unbranded stimuli associating with smartphone products: comparison of several machine learning algorithms”. Neural Computing and Applications 33: 4579–4593, (2021).
• [19] Hasoon, J. N., Fadel, A. H., Hameed, R. S., Mostafa, S. A., Khalaf, B. A., Mohammed, M. A., & Nedoma, J., “COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images”, Results in Physics, 31: 105045, (2021).
• [20] Alkan, A., “EEG işaretlerinin klasik ve modern yöntemlerle önişlenmesi ve sınıflandırılması”, Ph.D. Thesis, Sakarya University Instıtute of Scıence and Technology, Sakarya, (2005).
• [21] Ekersular, M.N., “Bilgisayarlı tomografi (BT) görüntülerinde safra kesesi taşının morfometrik özelliklerinin belirlenmesi ve karaciğer içi safra kanallarının tespiti”, MSc. Thesis, Kahramanmaraş Sütçü İmam University, Institute of Science and Technology, Kahramanmaraş, 47-49, (2019).
• [22] Bradley, A., “The use of the area under the ROC curve in the evaluation of machine learning algorithms”, Pattern Recognition, 30(7): 1145-1159, (1997).