Classification of Covid-19 Based on a Combination of GLCM and Deep Features by Using X-Ray Images

Yıl 2022, Cilt: 10 Sayı: 1, 313 - 325, 10.03.2022

Tolga Hayıt , Gökalp Çınarer

https://doi.org/10.33715/inonusaglik.1015407

Cited By: 2

Öz

With the coronavirus epidemic (Covid-19) affecting the whole world, urgent but accurate and fast diagnostic methods have been needed for viral diseases such as Covid-19. With the emergence of Covid-19, lung tomography and X-Ray images have been begun to be used by medical doctors to detect Covid-19. It is known that traditional and modern machine learning approaches using X-Ray and tomography images are used for disease diagnosis. In this respect, applications based on artificial intelligence contribute to the sector by showing similar or even better performances to field experts. In this study, for disease diagnosis by using X-Ray lung images, a hybrid support vector machines (SVM) classification model based on the combination of deep and traditional tissue analysis features is proposed. The dataset has been used consists of lung images of healthy, Covid-19, viral pneumonia and lung opacity patients. Hybrid features obtained from X-Ray images have been obtained by using Gray Level Co-occurrence Matrix (GLCM) and DenseNet-201 deep neural network. The performance of hybrid features has been compared to GLCM features as a traditional approach. Both attributes have been trained with SVM. An average of 99.2% accuracy has been achieved in classification success. Other performance measures which have been obtained show that hybrid features are more successful than the traditional method. The proposed artificial intelligence-based method for the diagnosis of Covid-19 has been shown to be promising.

Anahtar Kelimeler

Artificial intelligence, Covid-19, Deep learning, Hybrid model

X-RAY GÖRÜNTÜLERİNİ KULLANARAK GLCM VE DERİN ÖZNİTELİKLERİN BİRLEŞİMİNE DAYALI COVID-19 SINIFLANDIRILMASI

Öz

Koronavirüs salgınının (Covid-19) tüm dünyayı etkisi altına alması ile Covid-19 gibi viral hastalıklar için acil ancak doğru ve hızlı teşhis yöntemlerine ihtiyaç duyulmuştur. Covid-19’un ortaya çıkması ile birlikte Covid-19’un tespit edilmesi için tıp doktorları tarafından akciğer tomografi ve X-Ray görüntüleri kullanılmaya başlanmıştır. Geleneksel ve modern makine öğrenimi yaklaşımlarının X-Ray ve tomografi görüntüleri kullanılarak hastalık teşhisi için kullanıldığı bilinmektedir. Bu yönü ile yapay zekaya dayalı uygulamalar alan uzmanlarına benzer ve hatta neredeyse daha iyi performanslar ortaya koyarak sektöre katkı sağlamaktadır. Bu çalışmada X-Ray akciğer görüntüleri kullanılarak hastalık teşhisi için derin ve geleneksel doku analizi özniteliklerinin kombinasyonuna dayalı hibrit bir destek vektör makineleri (SVM) sınıflandırma modeli önerilmektedir. Çalışmada kullanılan veri seti, sağlıklı, Covid-19, viral pnömoni ve akciğer opasitesi hastalarının X-Ray akciğer görüntülerinden oluşmaktadır. X-Ray görüntülerinden elde edilen hibrit öznitelikler Gri Seviye Eş-Oluşum Matrisi (GLCM) ve DenseNet-201 derin sinir ağı kullanılarak elde edilmiştir. Hibrit özniteliklerin performansı, geleneksel bir yaklaşım olarak GLCM öznitelikleri ile karşılaştırılmıştır. Her iki öznitelik SVM ile eğitilmiştir. Sınıflandırma başarısında ortalama %99.2 doğruluk değerine ulaşılmıştır. Elde edilen diğer performans ölçütleri de hibrit özniteliklerin geleneksel yönteme göre daha başarılı olduğunu göstermektedir. Covid-19 teşhisi için önerilen yapay zekâ tabanlı yöntemin umut verici olduğu görülmüştür.

Anahtar Kelimeler

Covid-19, Derin öğrenme, Hibrit model, Yapay zekâ

Kaynakça

• Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., ... & Xia, L. (2020). Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 296(2), E32-E40.
• Ali, R., Hardie, R. C., De Silva, M. S., & Kebede, T. M. (2019). Skin lesion segmentation and classification for ISIC 2018 by combining deep CNN and handcrafted features. arXiv preprint arXiv:1908.05730.
• Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640.
• Barstugan, M., Ozkaya, U., & Ozturk, S. (2020). Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424.
• Chakraborty, S., Paul, S., & Rahat-uz-Zaman, M. (2021, January). Prediction of Apple Leaf Diseases Using Multiclass Support Vector Machine. In 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 147-151). IEEE.
• Chowdhury, M. E. H., Rahman, T. & Khandakar, A. (2021). COVID-19 Radiography Database. 20 Ocak 2022 tarihinde https://www.kaggle.com/tawsifurrahman/covid19-radiography-database adresinden erişildi.
• Chowdhury, M. E. H.., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., ... & Islam, M. T. (2020). Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access, 8, 132665-132676.
• COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) Eylül 24, 2021, tarihinde https://gisanddata.maps.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6 adresinden erişildi.
• De Siqueira, F. R., Schwartz, W. R., & Pedrini, H. (2013). Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing, 120, 336-345.
• Durmaz, B. (2020). COVID-19 Enfeksiyonunda Mikrobiyolojik Tanı. YIU Saglik Bil Derg, 1, 12-17.
• El Asnaoui, K., Chawki, Y., & Idri, A. (2021). Automated methods for detection and classification pneumonia based on x-ray images using deep learning. In Artificial Intelligence and Blockchain for Future Cybersecurity Applications (pp. 257-284). Springer, Cham.
• Goyal, P., Choi, J. J., Pinheiro, L. C., Schenck, E. J., Chen, R., Jabri, A., ... & Safford, M. M. (2020). Clinical characteristics of Covid-19 in New York city. New England Journal of Medicine, 382(24), 2372-2374.
• Hall, L. O., Paul, R., Goldgof, D. B., & Goldgof, G. M. (2020). Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:2004.02060.
• Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5), 786-804. Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
• Hasan, A. M., Jalab, H. A., Meziane, F., Kahtan, H., & Al-Ahmad, A. S. (2019). Combining deep and handcrafted image features for MRI brain scan classification. IEEE Access, 7, 79959-79967.
• Ho, D., Liang, E., Chen, X., Stoica, I., & Abbeel, P. (2019, May). Population based augmentation: Efficient learning of augmentation policy schedules. In International Conference on Machine Learning (pp. 2731-2741). PMLR.
• Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
• Jia, X., & Meng, M. Q. H. (2017, July). Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features. In 2017 39th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3154-3157). IEEE.
• Kareem, O., Al-Sulaifanie, A., Hasan, D. A., & Ahmed, D. M. (2021). Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review,". Asian J. Res. Comput. Sci, 10, 51-60.
• Kim, K. I., Jung, K., Park, S. H., & Kim, H. J. (2002). Support vector machines for texture classification. IEEE transactions on pattern analysis and machine intelligence, 24(11), 1542-1550.
• Luz, J. S., Oliveira, M. C., Araujo, F. H., & Magalhães, D. M. (2021). Ensemble of handcrafted and deep features for urban sound classification. Applied Acoustics, 175, 107819.
• Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020). 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, 122, 103869.
• Manjunath, B. S., & Ma, W. Y. (1996). Texture features for browsing and retrieval of image data. IEEE Transactions on pattern analysis and machine intelligence, 18(8), 837-842.
• Metre, V., & Ghorpade, J. (2013). An overview of the research on texture based plant leaf classification. arXiv preprint arXiv:1306.4345.
• Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in plant science, 7, 1419.
• Moreno-Barea, F. J., Jerez, J. M., & Franco, L. (2020). Improving classification accuracy using data augmentation on small data sets. Expert Systems with Applications, 161, 113696
• Nanni, L., De Luca, E., Facin, M. L., & Maguolo, G. (2020). Deep Learning and Handcrafted Features for Virus Image Classification. Journal of Imaging, 6(12), 143.
• Naz, J., Sharif, M., Raza, M., Shah, J. H., Yasmin, M., Kadry, S., & Vimal, S. (2021). Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Processing Letters, 1-26.
• Nikoo, H., Talebi, H., & Mirzaei, A. (2011, November). A supervised method for determining displacement of gray level co-occurrence matrix. In 2011 7th Iranian conference on machine vision and image processing (pp. 1-5). IEEE.
• Oyelade, O. N., & Ezugwu, A. E. (2021). A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images. Biomedical Signal Processing and Control, 65, 102366.
• Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in biology and medicine, 121, 103792.
• Pantic, I., Dimitrijevic, D., Nesic, D., & Petrovic, D. (2016). Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture. Journal of theoretical biology, 406, 124-128.
• Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S. B. A., ... & Chowdhury, M. E. H. (2021). Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in biology and medicine, 132, 104319.
• Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features.
• Soh, L. K., & Tsatsoulis, C. (1999). Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on geoscience and remote sensing, 37(2), 780-795.
• Tang, Z., Zhao, W., Xie, X., Zhong, Z., Shi, F., Liu, J., & Shen, D. (2020). Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv preprint arXiv:2003.11988.
• Tian, X., Ding, C. H., Chen, S., Luo, B., & Wang, X. (2021). Regularization graph convolutional networks with data augmentation. Neurocomputing, 436, 92-102.
• Uppuluri, A. (2021). GLCM texture features (https://www.mathworks.com/matlabcentral/fileexchange/22187-glcm-texture-features), MATLAB Central File Exchange. Retrieved September 16, 2021.
• Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
• Wang, S. H., & Zhang, Y. D. (2020). DenseNet-201-based deep neural network with composite learning factor and precomputation for multiple sclerosis classification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16(2s), 1-19.
• Wang, Z., Li, M., Wang, H., Jiang, H., Yao, Y., Zhang, H., & Xin, J. (2019). Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access, 7, 105146-105158. Wei, L., Su, R., Wang, B., Li, X., Zou, Q., & Gao, X. (2019). Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites. Neurocomputing, 324, 3-9.
• Wu, J., & Hicks, C. (2021). Breast Cancer Type Classification Using Machine Learning. Journal of personalized medicine, 11(2), 61.
• Wu, Z., & McGoogan, J. M. (2020). Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. Jama, 323(13), 1239-1242.
• Yeni Koronavirüs (SARS-CoV-2) nedir? (n.d.). T.C. Sağlık Bakanlığı. Eylül 24, 2021, tarihinde https://covid19.saglik.gov.tr/TR-66135/1-yeni-koronavirus-sars-cov-2-nedir.html adresinden erişildi.
• Yogeshwari, M., & Thailambal, G. (2021). Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks. Materials Today: Proceedings.
• Zhang, J., Xia, Y., Xie, Y., Fulham, M., & Feng, D. D. (2017). Classification of medical images in the biomedical literature by jointly using deep and handcrafted visual features. IEEE journal of biomedical and health informatics, 22(5), 1521-1530.