Abstract
Koronavirüsle ilk olarak Aralık 2019'da Çin'in Wuhan kentinde ortaya çıkmıştır. Bugüne kadar etkisini artırmaya devam ettirmiştir. Birçok kişinin ölümüne neden olan bu virüsün tespiti günümüzde büyük önem taşımaktadır. Bu hastalığın tespiti için birçok yaklaşım bulunmaktadır. Bu yaklaşımların en başarılılarında biri göğüs röntgeni görüntüleri kullanılarak koranavirüs hastalığının veya hastalarının tespitidir. Bu çalışmada, göğüs röntgeni görüntüleri kullanılarak normal, zatürre ve koranavirüs hastalarını sınıflandırmak için akıllı bir sistem önerilmiştir. Önerilen sistem dört aşamadan oluşmaktadır. İlk olarak, veri setindeki tüm görüntüler ön işleme tabi tutulmuştur. Daha sonra özellik çıkarımı için tek tip Yerel İkili Örüntü (LBP) ve DenseNet201 derin öğrenme modelleri kullanılmıştır. Etkili öznitelikleri seçmek için parçacık sürü optimizasyonu (PSO) algoritması kullanılmıştır. Belirlenen etkin özellikler, destek vektör makinesi (SVM) ile sınıflandırılmıştır. Performans kriteri olarak doğruluk ve AUC parametreleri kullanılmıştır. Sonuç olarak, doğruluk değeri 99.9% ve AUC değeri ise 1.00 olarak bulunmuştur. Veri seti ve önerilen model kodları verilen adreste herkese açık hale getirilmiştir: https://github.com/mfatiho/covid-detection-chest-xray
Keywords
Supporting Institution
İnönü Üniversitesi Bilimsel Araştırma Programı
Project Number
FYL-2021-2449
References
- Clerc, M. (2010). Particle Swarm Optimization. Particle Swarm Optimization, 1942–1948. https://doi.org/10.1002/9780470612163
- Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020). COVID-19 Image Data Collection: Prospective Predictions Are the Future. http://arxiv.org/abs/2006.11988
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
- COVID-19 Radiography Database | Kaggle. (n.d.). Retrieved April 14, 2021, from https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
- Cucinotta, D., & Vanelli, M. (2020). WHO declares COVID-19 a pandemic. Acta Biomedica, 91(1), 157–160. https://doi.org/10.23750/abm.v91i1.9397
- Göreke, V., Sarı, V., & Kockanat, S. (2021). A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings. Applied Soft Computing, 106, 107329. https://doi.org/10.1016/j.asoc.2021.107329
- Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708–1720. https://doi.org/10.1056/nejmoa2002032
- Hanbay, D. (2009). An expert system based on least square support vector machines for diagnosis of the valvular heart disease. Expert Systems with Applications, 36(3 PART 1), 4232–4238. https://doi.org/10.1016/j.eswa.2008.04.010
- Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 2261–2269. https://doi.org/10.1109/CVPR.2017.243
- Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., & Ng, A. Y. (2019). CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. http://arxiv.org/abs/1901.07031
- Ismael, A. M., & Şengür, A. (2021). Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications, 164(March 2020). https://doi.org/10.1016/j.eswa.2020.114054
- Karakanis, S., & Leontidis, G. (2021). Lightweight deep learning models for detecting COVID-19 from chest X-ray images. Computers in Biology and Medicine, 130(November 2020), 104181. https://doi.org/10.1016/j.compbiomed.2020.104181
- Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1), 51–59. https://doi.org/10.1016/0031-3203(95)00067-4
- Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987. https://doi.org/10.1109/TPAMI.2002.1017623
- Varela-Santos, S., & Melin, P. (2021). A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Information Sciences, 545, 403–414. https://doi.org/10.1016/j.ins.2020.09.041
- Wu, X., Chen, C., Zhong, M., Wang, J., & Shi, J. (2021). COVID-AL: The diagnosis of COVID-19 with deep active learning. Medical Image Analysis, 68, 101913. https://doi.org/10.1016/j.media.2020.101913
Abstract
The world first met the coronavirus (COVID-19) in Wuhan, China in December 2019. It has continued to increase its influence from the first encounter until today. The detection of this virus, which has caused the death of many, is of great importance today. There are many approaches to the detection of this disease. One of the most effective of these approaches is the detection of COVID-19 disease using chest X-Ray images. In this paper, an intelligent system was proposed to classify normal, pneumonia patients and COVID-19 patients using chest X-Ray images. The proposed system was composed of four stage. At first, all images in the dataset were pre-processed. Then for the feature extraction uniform Local Binary Pattern (LBP) and DenseNet201 deep learning models were used. Particle swarm optimization (PSO) algorithm was used to select effective features. The determined effective features were classified by support vector machine (SVM). Accuracy and AUC parameters were used as performance criteria. Evaluated accuracy and AUC values were 99.9%, 1.00, respectively. The dataset and proposed model codes are made publicly available at: https://github.com/mfatiho/covid-detection-chest-xray
Keywords
Project Number
FYL-2021-2449
References
- Clerc, M. (2010). Particle Swarm Optimization. Particle Swarm Optimization, 1942–1948. https://doi.org/10.1002/9780470612163
- Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020). COVID-19 Image Data Collection: Prospective Predictions Are the Future. http://arxiv.org/abs/2006.11988
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
- COVID-19 Radiography Database | Kaggle. (n.d.). Retrieved April 14, 2021, from https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
- Cucinotta, D., & Vanelli, M. (2020). WHO declares COVID-19 a pandemic. Acta Biomedica, 91(1), 157–160. https://doi.org/10.23750/abm.v91i1.9397
- Göreke, V., Sarı, V., & Kockanat, S. (2021). A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings. Applied Soft Computing, 106, 107329. https://doi.org/10.1016/j.asoc.2021.107329
- Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708–1720. https://doi.org/10.1056/nejmoa2002032
- Hanbay, D. (2009). An expert system based on least square support vector machines for diagnosis of the valvular heart disease. Expert Systems with Applications, 36(3 PART 1), 4232–4238. https://doi.org/10.1016/j.eswa.2008.04.010
- Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 2261–2269. https://doi.org/10.1109/CVPR.2017.243
- Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., & Ng, A. Y. (2019). CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. http://arxiv.org/abs/1901.07031
- Ismael, A. M., & Şengür, A. (2021). Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications, 164(March 2020). https://doi.org/10.1016/j.eswa.2020.114054
- Karakanis, S., & Leontidis, G. (2021). Lightweight deep learning models for detecting COVID-19 from chest X-ray images. Computers in Biology and Medicine, 130(November 2020), 104181. https://doi.org/10.1016/j.compbiomed.2020.104181
- Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1), 51–59. https://doi.org/10.1016/0031-3203(95)00067-4
- Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987. https://doi.org/10.1109/TPAMI.2002.1017623
- Varela-Santos, S., & Melin, P. (2021). A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Information Sciences, 545, 403–414. https://doi.org/10.1016/j.ins.2020.09.041
- Wu, X., Chen, C., Zhong, M., Wang, J., & Shi, J. (2021). COVID-AL: The diagnosis of COVID-19 with deep active learning. Medical Image Analysis, 68, 101913. https://doi.org/10.1016/j.media.2020.101913
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | PAPERS |
| Authors | |
| Project Number | FYL-2021-2449 |
| Publication Date | October 10, 2022 |
| Submission Date | September 8, 2022 |
| Acceptance Date | September 16, 2022 |
| Published in Issue | Year 2022 Volume: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium |
The Creative Commons Attribution 4.0 International License
is applied to all research papers published by JCS and
A Digital Object Identifier (DOI) 
is assigned for each published paper.