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FUSED DEEP FEATURES BASED CLASSIFICATION FRAMEWORK FOR COVID-19 CLASSIFICATION WITH OPTIMIZED MLP

Year 2020, Volume: 8 , 15 - 27, 31.12.2020
https://doi.org/10.36306/konjes.821782

Abstract

The new type of Coronavirus disease called COVID-19 continues to spread quite rapidly. Although it shows some specific symptoms, this disease, which can show different symptoms in almost every individual, has caused hundreds of thousands of patients to die. Although healthcare professionals work hard to prevent further loss of life, the rate of disease spread is very high. For this reason, the help of computer aided diagnosis (CAD) and artificial intelligence (AI) algorithms is vital. In this study, a method based on optimization of convolutional neural network (CNN) architecture, which is the most effective image analysis method of today, is proposed to fulfill the mentioned COVID-19 detection needs. First, COVID-19 images are trained using ResNet-50 and VGG-16 architectures. Then, features in the last layer of these two architectures are combined with feature fusion. These new image features matrices obtained with feature fusion are classified for COVID detection. A multi-layer perceptron (MLP) structure optimized by the whale optimization algorithm is used for the classification process. The obtained results show that the performance of the proposed framework is almost 4.5% higher than VGG-16 performance and almost 3.5% higher than ResNet-50 performance.

References

  • Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020). Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. arXiv preprint arXiv:2004.02696.
  • Albahri O.S., Zaidan A.A., Albahri A.S.,. Zaidan B.B, Abdulkareem K. H., Al-qaysi Z.T., Alamoodi A.H., Aleesa A.M., Chyad M.A., Alesa R.M., Kem L.C., Lakulu M. M., Ibrahim A.B., Rashid N. A. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13 (10), 1381-1396.
  • Barstugan, M., Ozkaya, U., & Ozturk, S. (2020). Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424.
  • Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Lu, S., & Sahlol, A. T. (2020). New machine learning method for image-based diagnosis of COVID-19. Plos one, 15(6), e0235187.
  • Fan, D. P., Zhou, T., Ji, G. P., Zhou, Y., Chen, G., Fu, H., ... & Shao, L. (2020). Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Hemdan, E. E. D., Shouman, M. A., & Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.
  • Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020). Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics, 1-8.
  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
  • Nour, M., Cömert, Z., & Polat, K. (2020). A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Applied Soft Computing, 106580.
  • Pereira R. M., Bertolini D., Teixeira L. O., Silla C. N., Costa Y. M.G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194.
  • Pham, T.D. (2020). A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Nature, Sci Rep 10, 16942.
  • Randhawa, G. S., Soltysiak, M. P., El Roz, H., de Souza, C. P., Hill, K. A., & Kari, L. (2020). Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. Plos one, 15(4), e0232391.
  • Sahlol, A. T., Yousri, D., Ewees, A. A., Al-Qaness, M. A., Damasevicius, R., & Abd Elaziz, M. (2020). COVID-19 image classification using deep features and fractional-order marine predators algorithm. Scientific Reports, 10(1), 1-15.
  • Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., ... & Shen, D. (2020). Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:2003.09860.
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Singh, D., Kumar, V., Yadav, V., & Kaur, M. (2020). Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images. International Journal of Pattern Recognition and Artificial Intelligence, 2151004.
  • Soares, E., Angelov, P., Biaso, S., Froes, M. H., & Abe, D. K. (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv.
  • Sun, L., Mo, Z., Yan, F., Xia, L., Shan, F., Ding, Z., ... & Yuan, H. (2020). Adaptive feature selection guided deep forest for covid-19 classification with chest ct. IEEE Journal of Biomedical and Health Informatics.
  • 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, 103792.
  • Öztürk, Ş., & Özkaya, U. (2020). Gastrointestinal tract classification using improved LSTM based CNN. Multimedia Tools and Applications, 1-16.
  • Öztürk, Ş., Özkaya, U., & Barstuğan, M. (2020). Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features. International Journal of Imaging Systems and Technology.
  • Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images. Medical Hypotheses, 109761.
  • Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews.
  • Wang X., Deng X., Fu Q., Zhou Q., Feng J., Ma H., Liu W., and Zheng C. (2020). A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT. IEEE Transactions on Medical Imaging, 39(8) , 2615-2625.
  • Wu, Z., Ling, Q., Chen, T., & Giannakis, G. B. (2020). Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks. IEEE Transactions on Signal Processing, 68, 4583-4596.
  • Zu, Z. Y., Jiang, M. D., Xu, P. P., Chen, W., Ni, Q. Q., Lu, G. M., & Zhang, L. J. (2020). Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology, 200490.

Optimize Edilmiş ÇKA ile Covıd-19 Sınıflandırması için Kaynaştırılmış Derin Özelliklere Dayalı Sınıflandırma Çerçevesi

Year 2020, Volume: 8 , 15 - 27, 31.12.2020
https://doi.org/10.36306/konjes.821782

Abstract

COVID-19 adı verilen yeni tip Koronavirüs hastalığı oldukça hızlı yayılmaya devam etmektedir. Bazı spesifik semptomlar gösterse de hemen her bireyde farklı semptomlar gösterebilen bu hastalık yüzbinlerce hastanın hayatını kaybetmesine neden olmuştur. Sağlık uzmanları, daha fazla yaşam kaybını önlemek için çok çalışsalar da, hastalık yayılma oranı çok yüksektir. Bu nedenle Bilgisayar Destekli Teşhis (BDT) ve Yapay Zeka (YZ) algoritmalarının desteği hayati önem taşımaktadır. Bu çalışmada, belirtilen COVID-19 algılama ihtiyaçlarını karşılamak için günümüzün en etkili görüntü analiz yöntemi olan Evrişimli Sinir Ağı (ESA) mimarisinin optimizasyonuna dayalı bir yöntem önerilmiştir. İlk olarak, COVID-19 görüntüleri ResNet-50 ve VGG-16 mimarileri kullanılarak eğitilir. Ardından, bu iki mimarinin son katmanındaki özellikler füzyon işlemi uygulanmıştır. Füzyon işlemi ile elde edilen bu yeni görüntü özellikleri matrisleri, COVID-19 tespiti için sınıflandırılır. Sınıflandırma işlemi için Balina Optimizasyon Algoritması (BOA) ile optimize edilmiş Çok Katmanlı Bir Algılayıcı (ÇKA) yapısı kullanılır. Elde edilen sonuçlar, önerilen çerçevenin performansının VGG-16 performansından neredeyse % 4,5 ve ResNet-50 performansından neredeyse % 3,5 daha yüksek olduğunu göstermektedir.

References

  • Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020). Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. arXiv preprint arXiv:2004.02696.
  • Albahri O.S., Zaidan A.A., Albahri A.S.,. Zaidan B.B, Abdulkareem K. H., Al-qaysi Z.T., Alamoodi A.H., Aleesa A.M., Chyad M.A., Alesa R.M., Kem L.C., Lakulu M. M., Ibrahim A.B., Rashid N. A. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13 (10), 1381-1396.
  • Barstugan, M., Ozkaya, U., & Ozturk, S. (2020). Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424.
  • Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Lu, S., & Sahlol, A. T. (2020). New machine learning method for image-based diagnosis of COVID-19. Plos one, 15(6), e0235187.
  • Fan, D. P., Zhou, T., Ji, G. P., Zhou, Y., Chen, G., Fu, H., ... & Shao, L. (2020). Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Hemdan, E. E. D., Shouman, M. A., & Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.
  • Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020). Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics, 1-8.
  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
  • Nour, M., Cömert, Z., & Polat, K. (2020). A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Applied Soft Computing, 106580.
  • Pereira R. M., Bertolini D., Teixeira L. O., Silla C. N., Costa Y. M.G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194.
  • Pham, T.D. (2020). A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Nature, Sci Rep 10, 16942.
  • Randhawa, G. S., Soltysiak, M. P., El Roz, H., de Souza, C. P., Hill, K. A., & Kari, L. (2020). Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. Plos one, 15(4), e0232391.
  • Sahlol, A. T., Yousri, D., Ewees, A. A., Al-Qaness, M. A., Damasevicius, R., & Abd Elaziz, M. (2020). COVID-19 image classification using deep features and fractional-order marine predators algorithm. Scientific Reports, 10(1), 1-15.
  • Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., ... & Shen, D. (2020). Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:2003.09860.
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Singh, D., Kumar, V., Yadav, V., & Kaur, M. (2020). Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images. International Journal of Pattern Recognition and Artificial Intelligence, 2151004.
  • Soares, E., Angelov, P., Biaso, S., Froes, M. H., & Abe, D. K. (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv.
  • Sun, L., Mo, Z., Yan, F., Xia, L., Shan, F., Ding, Z., ... & Yuan, H. (2020). Adaptive feature selection guided deep forest for covid-19 classification with chest ct. IEEE Journal of Biomedical and Health Informatics.
  • 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, 103792.
  • Öztürk, Ş., & Özkaya, U. (2020). Gastrointestinal tract classification using improved LSTM based CNN. Multimedia Tools and Applications, 1-16.
  • Öztürk, Ş., Özkaya, U., & Barstuğan, M. (2020). Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features. International Journal of Imaging Systems and Technology.
  • Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images. Medical Hypotheses, 109761.
  • Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews.
  • Wang X., Deng X., Fu Q., Zhou Q., Feng J., Ma H., Liu W., and Zheng C. (2020). A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT. IEEE Transactions on Medical Imaging, 39(8) , 2615-2625.
  • Wu, Z., Ling, Q., Chen, T., & Giannakis, G. B. (2020). Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks. IEEE Transactions on Signal Processing, 68, 4583-4596.
  • Zu, Z. Y., Jiang, M. D., Xu, P. P., Chen, W., Ni, Q. Q., Lu, G. M., & Zhang, L. J. (2020). Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology, 200490.
There are 27 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Şaban Öztürk 0000-0003-2371-8173

Enes Yiğit 0000-0002-0960-5335

Umut Özkaya 0000-0002-9244-0024

Publication Date December 31, 2020
Submission Date November 5, 2020
Acceptance Date December 3, 2020
Published in Issue Year 2020 Volume: 8

Cite

IEEE Ş. Öztürk, E. Yiğit, and U. Özkaya, “FUSED DEEP FEATURES BASED CLASSIFICATION FRAMEWORK FOR COVID-19 CLASSIFICATION WITH OPTIMIZED MLP”, KONJES, vol. 8, pp. 15–27, 2020, doi: 10.36306/konjes.821782.