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Covid-19 Hastalığının Teşhisi için CNN Tabanlı Modeller ile Adaboost Algoritmasının Kombinasyonunun Performans Analizi

Year 2023, , 179 - 190, 27.03.2023
https://doi.org/10.2339/politeknik.901375

Abstract

2019 yılı sonunda yeni bir Coronavirüs formu olan Covid-19 tüm dünyada hızlı bir şekilde yayıldı. Bu hastalığın artan günlük vakaları ile hızlı, güvenilir ve otomatik tespit sistemlerine olan ihtiyaç arttı. Bu nedenle, bu çalışma, göğüs kafesi röntgen görüntülerini sınıflandırmak için makine öğrenmesi algoritmalarından biri olan Adaboost algoritması ile Evrişimsel Sinir Ağları’nı (CNN) birleştiren yeni bir teknik önermektedir. Adaboost algoritmasının eğitim için ihtiyaç duyduğu özellikler temel CNN algoritması ve önceden eğitilmiş ResNet-152 ile göğüs kafesi röntgen görüntülerinden ayrı ayrı elde edilmiştir. Adaboost algoritmasında bu iki farklı özellik çıkarma yöntemini karşılaştırmak için farklı öğrenme oranı değerleri ve tahmin sayısı kullanılmıştır. Bu teknikler, Normal, Viral Zatürre ve Covid-19 olarak etiketlenmiş göğüs röntgeni görüntülerini içeren veri setinde uygulanmıştır. Kullanılan veri seti sınıflar arasında dengesiz olduğundan, sınıfların görüntü sayısını dengelemek için SMOTE yöntemi kullanılmıştır. Bu çalışma, Adaboost algoritmasında otomatik özellik çıkarıcı olarak kullanılan, önerilen CNN modelin (öğrenme oranı 0.1 ve tahminci sayısı 25) % 94.5 doğruluk,% 93 kesinlik,% 94 duyarlılık ve % 93 F1 skoru değerleri ile daha yüksek sınıflandırma performansı sağladığını göstermektedir.

References

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  • [22] Cohen J.P., Morrison P., Dao L., "COVID-19 image data collection", arXiv:2003.11597, 2020 https://github.com/ieee8023/covid-chestxray-dataset.
  • [23] https://github.com/armiro/COVID-CXNet.
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Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease

Year 2023, , 179 - 190, 27.03.2023
https://doi.org/10.2339/politeknik.901375

Abstract

At the end of 2019, Covid-19, which is a new form of Coronavirus, has spread widely all over the world. With the increasing daily cases of this disease, fast, reliable, and automatic detection systems have been more crucial. Therefore, this study proposes a new technique that combines the machine learning algorithm of Adaboost with Convolutional Neural Networks (CNN) to classify Chest X-Ray images. Basic CNN algorithm and pretrained ResNet-152 have been used separately to obtain features of the Adaboost algorithm from Chest X-Ray images. Several learning rates and the number of estimators have been used to compare these two different feature extraction methods on the Adaboost algorithm. These techniques have been applied to the dataset, which contains Chest X-Ray images labeled as Normal, Viral Pneumonia, and Covid-19. Since the used dataset is unbalanced between classes SMOTE method has been used to make the number of images of classes balance. This study shows that proposed CNN as a feature extractor on the Adaboost algorithm(learning rate of 0.1 and 25 estimators) provides higher classification performance with 94.5% accuracy, 93% precision, 94% recall, and 93% F1-score.

References

  • [1] Üstün Ç. , Özçi̇ftçi̇ S. “COVID-19 Pandemisinin Sosyal Yaşam ve Etik Düzlem Üzerine Etkileri: Bir Değerlendirme Çalışması.” Anatolian Clinic the Journal of Medical Sciences.; 25(Special Issue on COVID 19): 142-153.
  • [2] İşsever, H. , İşsever, T. , Öztan, G. “COVID-19 Epidemiyolojisi.” Sağlık Bilimlerinde İleri Araştırmalar Dergisi, 1-13, (2020).
  • [3] İlhan A. “SARS-COV-2 VE COVID-19 PATOGENEZİ.” Gazi Sağlık Bilimleri Dergisi; 78-87, (2020).
  • [4] Narin, Ali & Kaya, Ceren & Pamuk, Ziynet. ''Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks'', (2020).
  • [5] Erdaş Ç., Ölçer D., “Detection and differentiation of COVID-19 using deep learning approach fed by x-rays.” International Journal of Applied Mathematics Electronics and Computers,8(3): 97-101, (2020).
  • [6] Wang G., Teoh J. Y. -C. and Choi K. -S., "Diagnosis of prostate cancer in a Chinese population by using machine learning methods," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 1-4, (2018).
  • [7] Akalın B., Veranyurt Ü., Veranyurt O. “Classification of individuals at risk of heart disease using machine learning .” Cumhuriyet Medical Journal , 42 (3) , 283-289, (2020).
  • [8] Rajpurkar P. et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” Nov. (2017).
  • [9] Luz E., Silva P. L., Silva R., Silva L., Moreira G. and Menotti D., “Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images”. Apr. (2020).
  • [10] Shan F. et al., “Lung Infection Quantification of COVID-19 in CT Images with Deep Learning” arXiv:2003.04655.(2020).
  • [11] M. Sevi and İ. AYDIN, "COVID-19 Detection Using Deep Learning Methods," 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 1-6, (2020).
  • [12] I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks", Physical and Engineering Sciences in Medicine, (2020).
  • [13] L. Wang and A. Wong, "Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images", arXiv, (2020).
  • [14] X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, L. Yu, Y. Chen, J. Su, G. Lang et al., Deep learning system to screen coronavirus disease 2019 pneumonia. arxiv (2020).
  • [15] Freund Y. and Schapire RE. “A decision-theoretic generalization of on-line learning and an application to boosting.” Journal of Computer and System Sciences, 55(1):119–139, (1997).
  • [16] Rojarath A., Songpan W. and Pong-inwong C., "Improved ensemble learning for classification techniques based on majority voting," 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, 107-110, (2016).
  • [17] Liu W., Zhang M., Luo V and Cai Y., "An Ensemble Deep Learning Method for Vehicle Type Classification on Visual Traffic Surveillance Sensors," in IEEE Access, vol. 5, 24417-24425, (2017).
  • [18] Tuğ Karoğlu, T , Okut, H . “Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods .” Cumhuriyet Science Journal , 41 (1) , 93-105, (2020).
  • [19] Ferreira A.J., Figueiredo M.A.T.”Boosting Algorithms: A Review of Methods, Theory, and Applications.” In: Zhang C., Ma Y. (eds) Ensemble Machine Learning. Springer, Boston, MA. Pham K., Kim D., Park S., Choi H., “Ensemble learning-based classification models for slope stability analysis.” CATENA, 196: (2012).
  • [20] Liwei W., Masashi S., Yang C., Zhou Z., Feng J., “On the Margin Explanation of Boosting Algorithms.” 21st Annual Conference on Learning Theory, COLT 2008. 479-490, (2008).
  • [21] Chowdhury M.E.H., Rahman T., Khandakar A., Mazhar R., Kadir M.A., Mahbub Z.B., Islam K.R., Khan M.S., Iqbal A., Al-Emadi N., Reaz M.B.I., Islam M. T., “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, 8: 132665 – 132676, (2020).
  • [22] Cohen J.P., Morrison P., Dao L., "COVID-19 image data collection", arXiv:2003.11597, 2020 https://github.com/ieee8023/covid-chestxray-dataset.
  • [23] https://github.com/armiro/COVID-CXNet.
  • [24] Yavaş M., Güran A., Uysal M., “Covid-19 Veri Kümesinin SMOTE Tabanlı Örnekleme Yöntemi Uygulanarak Sınıflandırılması .” Avrupa Bilim ve Teknoloji Dergisi , Ejosat Özel Sayı 2020 (HORA) , 258-264 . (2020).
  • [25] Cortes C., Vapnik V., “Support-vector networks, Mach. Learn.,” 20:273-297, (1995).
  • [26] Erpolat S., Öz E.. “Kanser Verilerinin Sınıflandırılmasında Yapay Sinir Ağları İle Destek Vektör Makineleri 'Nin Karşılaştırılması .” İstanbul Aydın Üniversitesi Dergisi , 2 (5) , 71-83, (2010).
  • [27] Deng J., Dong W., Socher R., Li LJ, Li K., Fei-Fei L. “ImageNet: A large-scale hierarchical image database, Computer Vision and Pattern Recognition,” 2009. CVPR 2009. IEEE Conference on, 248–255.(2009).
  • [28] Darıcı M.B., “Göğüs Kafesi Röntgen Görüntülerinde Derin Öğrenme Metoduyla Zatürre Hastalığının Tanısı.” Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü. (2020).
  • [29] He K., Zhang X., Ren S. & Sun J., “Deep Residual Learning for Image Recognition.” 770-778, (2016).
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Muazzez Buket Darıcı 0000-0002-0943-9381

Publication Date March 27, 2023
Submission Date April 2, 2021
Published in Issue Year 2023

Cite

APA Darıcı, M. B. (2023). Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease. Politeknik Dergisi, 26(1), 179-190. https://doi.org/10.2339/politeknik.901375
AMA Darıcı MB. Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease. Politeknik Dergisi. March 2023;26(1):179-190. doi:10.2339/politeknik.901375
Chicago Darıcı, Muazzez Buket. “Performance Analysis of Combination of CNN-Based Models With Adaboost Algorithm to Diagnose Covid-19 Disease”. Politeknik Dergisi 26, no. 1 (March 2023): 179-90. https://doi.org/10.2339/politeknik.901375.
EndNote Darıcı MB (March 1, 2023) Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease. Politeknik Dergisi 26 1 179–190.
IEEE M. B. Darıcı, “Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease”, Politeknik Dergisi, vol. 26, no. 1, pp. 179–190, 2023, doi: 10.2339/politeknik.901375.
ISNAD Darıcı, Muazzez Buket. “Performance Analysis of Combination of CNN-Based Models With Adaboost Algorithm to Diagnose Covid-19 Disease”. Politeknik Dergisi 26/1 (March 2023), 179-190. https://doi.org/10.2339/politeknik.901375.
JAMA Darıcı MB. Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease. Politeknik Dergisi. 2023;26:179–190.
MLA Darıcı, Muazzez Buket. “Performance Analysis of Combination of CNN-Based Models With Adaboost Algorithm to Diagnose Covid-19 Disease”. Politeknik Dergisi, vol. 26, no. 1, 2023, pp. 179-90, doi:10.2339/politeknik.901375.
Vancouver Darıcı MB. Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease. Politeknik Dergisi. 2023;26(1):179-90.
 
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