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Covid-19 Radyografi Veritabanı Kullanılarak Çok Öznitelikli Füzyona Sahip Hibrit Modellerin Karşılaştırılması

Year 2025, Volume: 27 Issue: 80, 326 - 336, 23.05.2025

Fatma Günseli Yaşar Çıklaçandır , Gözde Ulutagay

https://doi.org/10.21205/deufmd.2025278020

Abstract

COVID-19, dünya çapında milyonlarca kişiyi etkiledi. Şu anda azalmış gibi görünse de hastalığın farklı varyasyonları devam ediyor. Bu nedenle, COVID-19'u hızlı ve kesin bir şekilde teşhis etmek hala hayati önem taşıyor. Göğüs görüntülemenin, hastalığın erken evrelerinde bile COVID-19 enfeksiyonunu açıkça gösterdiği, doktorların ve radyologların daha hızlı ve daha doğru kararlar almasına yardımcı olduğu kanıtlanmıştır. Bu çalışma, enfekte hastaları sağlıklı insanlardan doğru bir şekilde ayırt etmek için Evrişimsel Sinir Ağı tabanlı modellere ve sınıflandırıcılara dayalı özellik füzyonuna sahip hibrit bir model önermektedir. İki farklı Evrişimsel Sinir Ağı tabanlı modelden çıkarılan özellikler birleştirilir veya özellik seçiminden önce eklenir. Dört sınıftan (Covid, Lung_Opacity, Normal ve Viral Pneumonia) 21.168 görüntüyü içeren, kamuya açık bir radyografi veritabanında, beş kat çapraz doğrulamayı kullanan kapsamlı testler yapılmıştır. Yapılan testlere göre yaklaşık %96 oranında doğruluk oranı elde edildi. Bulgular ayrıca önerilen yaklaşımın sağlık sistemlerinde hızla artan iş yüküne önemli ölçüde katkıda bulunabileceğini göstermektedir.

Keywords

Öznitelik Çıkarma Öznitelik Füzyonu Derin Öğrenme

References

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  • [25] Ayadi, M., Ksibi, A., Al-Rasheed, A., Soufiene, B.O., 2022. COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images. Healthcare, Vol.10.
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  • [29] Ibrokhimov, B., Kang, J.Y., 2022. Deep Learning Model for COVID-19-Infected Pneumonia Diagnosis Using Chest Radiography Images. BioMedInformatics, Vol.2, pp.654–670.
  • [30] Ji, D., Zhang, Z., Zhao, Y., Zhao, Q., 2021. Research on classification of COVID-19 chest X-ray image modal feature fusion based on deep learning. Journal of Healthcare Engineering.
  • [31] Narin, A., Kaya, C., Pamuk, Z., 2021. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications, Vol.24, pp.1207–1220.
  • [32] Hussain, E., Hasan, M., Rahman, M.A., Lee, I., Tamanna, T., Parvez, M.Z., 2021. CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos, Solitons & Fractals, Vol.142.
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  • [39] Aksoy, B., Salman, O.K.M., 2022. Prediction of COVID-19 disease with ResNet-101 deep learning architecture using computerized tomography images. Turkish Journal of Nature and Science, Vol.11(2), pp.36–42.
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Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database

Year 2025, Volume: 27 Issue: 80, 326 - 336, 23.05.2025

Fatma Günseli Yaşar Çıklaçandır , Gözde Ulutagay

https://doi.org/10.21205/deufmd.2025278020

Abstract

COVID-19, which emerged in 2019 and was subsequently classified as a pandemic, has affected millions of individuals worldwide. Different variations of the illness continue to persist, even though it may seem to have subsided at the moment. Hence, it remains essential to promptly and precisely diagnose COVID-19. Chest imaging has been proven to clearly demonstrate COVID-19 infection even in the early stages of the disease, assisting physicians and radiologists in making quicker and more accurate judgements. This study proposes a hybrid model with feature fusion based on Convolutional Neural Network based models and classifiers to accurately distinguish infected patients from healthy people. The extracted features from two different Convolutional Neural Network based models are concatenated, or added before feature selection. On a publicly accessible radiography database containing 21168 images of the four classes (Covid, Lung_Opacity, Normal, and Viral Pneumonia), extensive tests utilizing five fold cross-validation have been conducted. According to the tests, an accuracy rate of about 96% has been obtained. The findings also demonstrate that the proposed approach can contribute significantly to the rapidly expanding workload in health-care systems.

Keywords

Feature Extraction Feature Fusion Deep Learning

References

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  • [2] Bayram, F., Eleyan, A., 2022. COVID-19 detection on chest radiographs using feature fusion based deep learning. Signal, Image and Video Processing, Vol.16(6), pp.1455–1462.
  • [3] Çelik, G., Talu, M., 2021. Generating the image viewed from EEG signals. Pamukkale University Journal of Engineering Sciences, Vol.27(2).
  • [4] Altan, G., 2022. Breast cancer diagnosis using deep belief networks on ROI images. Pamukkale University Journal of Engineering Sciences, Vol.28(2), pp.286–291.
  • [5] Yuzkat, M., İlhan, H., Aydın, N., 2023. Detection of human sperm cells using deep learning-based object detection methods. Pamukkale University Journal of Engineering Sciences, Vol.30(4).
  • [6] Cevik, F., Kilimci, Z.H., 2020. The evaluation of Parkinson's disease with sentiment analysis using deep learning methods and word embedding models. Pamukkale University Journal of Engineering Sciences, Vol.27(2), pp.151–161.
  • [7] Akalın, F., Yumuşak, N., 2024. Detection of gastrointestinal anomalies with a deep learning-based ensemble classifier approach. Pamukkale University Journal of Engineering Sciences, Vol.30(3), pp.366–373.
  • [8] Kong, L., Cheng, J., 2022. Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion. Biomedical Signal Processing and Control, Vol.77.
  • [9] Sitaula, C., Hossaini, M.B., 2021. Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Applied Intelligence, Vol.51, pp.2850–2863.
  • [10] El-Kenawy, E.S.M., Ibrahim, A., Mirjalili, S., Eid, M.M., Hussein, S.E., 2020. Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images. IEEE Access, Vol.8, pp.179317–179335.
  • [11] El-Kenawy, E.S.M., Mirjalili, S., Ibrahim, A., Alrahmawy, M., El-Said, M., Zaki, R.M., Eid, M.M., 2021. Advanced meta-heuristics, convolutional neural networks, and feature selectors for efficient COVID-19 X-ray chest image classification. IEEE Access, Vol.9, pp.36019–36037.
  • [12] Reshi, A.A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., Alsuwailem, H., Choi, G.S., 2021. An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity, pp.1–12.
  • [13] Barshooi, A.H., Amirkhani, A., 2022. A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images. Biomedical Signal Processing and Control, Vol.72, Article 103326.
  • [14] Srinivas, K., Gagana Sri, R., Pravallika, K., Nishitha, K., Polamuri, S.R., 2024. COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images. Multimedia Tools and Applications, Vol.83(12), pp.36665–36682.
  • [15] El Houby, E.M., 2024. COVID‑19 detection from chest X-ray images using transfer learning. Scientific Reports, Vol.14(1), Article 11639.
  • [16] Abdullah, M., Berhe Abrha, F., Kedir, B., Tagesse, T.T., 2024. A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays. Heliyon, Vol.10(5).
  • [17] Shaban, W.M., Rabie, A.H., Saleh, A.I., Abo-Elsoud, M.A., 2020. A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems, Vol.205.
  • [18] Loey, M., Manogaran, G., Taha, M.H.N., Khalifa, N.E.M., 2021. A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, Vol.167.
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  • [21] Kaya, Y., Gürsoy, E., 2023. A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection. Soft Computing, Vol.27, pp.5521–5535.
  • [22] Nayak, S.R., Nayak, D.R., Sinha, U., Arora, V., Pachori, R.B., 2022. An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images. Diagnostics, Vol.13, Article 131.
  • [23] Sanida, T., Tabakis, I.M., Sanida, M.V., Sideris, A., Dasygenis, M., 2023. A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images. Information, Vol.14(6), Article 310.
  • [24] Sanida, T., Sideris, A., Tsiktsiris, D., Dasygenis, M., 2022. Lightweight neural network for COVID-19 detection from chest X-ray images implemented on an embedded system. Technologies, Vol.10, Article 37.
  • [25] Ayadi, M., Ksibi, A., Al-Rasheed, A., Soufiene, B.O., 2022. COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images. Healthcare, Vol.10.
  • [26] Hafeez, U., Umer, M., Hameed, A., Mustafa, H., Sohaib, A., Nappi, M., Madni, H.A., 2022. A CNN based coronavirus disease prediction system for chest X-rays. Journal of Ambient Intelligence and Humanized Computing, pp.1–15.
  • [27] Huang, M.L., Liao, Y.C., 2022. A lightweight CNN-based network on COVID-19 detection using X-ray and CT images. Computers in Biology and Medicine, Vol.146.
  • [28] Ghose, P., Uddin, A., Acharjee, U.K., Sharmin, S., 2022. Deep viewing for the identification of COVID-19 infection status from chest X-ray image using CNN based architecture. Intelligent Systems with Applications, Vol.16.
  • [29] Ibrokhimov, B., Kang, J.Y., 2022. Deep Learning Model for COVID-19-Infected Pneumonia Diagnosis Using Chest Radiography Images. BioMedInformatics, Vol.2, pp.654–670.
  • [30] Ji, D., Zhang, Z., Zhao, Y., Zhao, Q., 2021. Research on classification of COVID-19 chest X-ray image modal feature fusion based on deep learning. Journal of Healthcare Engineering.
  • [31] Narin, A., Kaya, C., Pamuk, Z., 2021. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications, Vol.24, pp.1207–1220.
  • [32] Hussain, E., Hasan, M., Rahman, M.A., Lee, I., Tamanna, T., Parvez, M.Z., 2021. CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos, Solitons & Fractals, Vol.142.
  • [33] 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, Vol.121.
  • [34] Khan, A.I., Shah, J.L., Bhat, M.M., 2020. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Computer Methods and Programs in Biomedicine, Vol.196.
  • [35] Khan, I.U., Aslam, N., 2020. A deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images. Information, Vol.11, Article 419.
  • [36] Rahman, T., 2021. COVID-19 radiography database. [Online] Available at: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.
  • [37] Kochgaven, C., Mishra, P., Shitole, S., 2021. Detecting presence of COVID-19 with ResNet-18 using PyTorch. International Conference on Communication Information and Computing Technology (ICCICT), pp.1–6.
  • [38] Yazan, E., Talu, M.F., 2023. Integration of attention mechanisms into segmentation architectures and their application on breast lymph node images. Pamukkale University Journal of Engineering Sciences, Vol.29(3), pp.248–255.
  • [39] Aksoy, B., Salman, O.K.M., 2022. Prediction of COVID-19 disease with ResNet-101 deep learning architecture using computerized tomography images. Turkish Journal of Nature and Science, Vol.11(2), pp.36–42.
  • [40] Al-Huseiny, M.S., Sajit, A.S., 2021. Transfer learning with GoogLeNet for detection of lung cancer. Indonesian Journal of Electrical Engineering and Computer Science, Vol.22(2), pp.1078–1086.
  • [41] Peng, H., Long, F., Ding, C., 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27(8), pp.1226–1238.
  • [42] Cai, Y., Huang, T., Hu, L., Shi, X., Xie, L., Li, Y., 2012. Prediction of lysine ubiquitination with mRMR feature selection and analysis. Amino Acids, Vol.42, pp.1387–1395.
  • [43] Cover, T., Hart, P., 1996. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, Vol.13(1), pp.21–27.
  • [44] Breiman, L., 1996. Bagging predictors. Machine Learning, Vol.24, pp.123–140.
  • [45] Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine Learning, Vol.20, pp.273–297.
  • [46] Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics, Vol.7(2), pp.179–188.
There are 46 citations in total.

Details

Primary Language English
Subjects Computer Vision and Multimedia Computation (Other)
Journal Section Research Article
Authors

Fatma Günseli Yaşar Çıklaçandır 0000-0001-6182-7173

Gözde Ulutagay 0000-0002-7415-4251

Early Pub Date May 12, 2025
Publication Date May 23, 2025
Submission Date September 15, 2024
Acceptance Date November 13, 2024
Published in Issue Year 2025 Volume: 27 Issue: 80

Cite

APA Yaşar Çıklaçandır, F. G., & Ulutagay, G. (2025). Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 27(80), 326-336. https://doi.org/10.21205/deufmd.2025278020
AMA Yaşar Çıklaçandır FG, Ulutagay G. Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database. DEUFMD. May 2025;27(80):326-336. doi:10.21205/deufmd.2025278020
Chicago Yaşar Çıklaçandır, Fatma Günseli, and Gözde Ulutagay. “Comparison of Hybrid Models With Multi-Feature Fusion Using Covid-19 Radiography Database”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 27, no. 80 (May 2025): 326-36. https://doi.org/10.21205/deufmd.2025278020.
EndNote Yaşar Çıklaçandır FG, Ulutagay G (May 1, 2025) Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 27 80 326–336.
IEEE F. G. Yaşar Çıklaçandır and G. Ulutagay, “Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database”, DEUFMD, vol. 27, no. 80, pp. 326–336, 2025, doi: 10.21205/deufmd.2025278020.
ISNAD Yaşar Çıklaçandır, Fatma Günseli - Ulutagay, Gözde. “Comparison of Hybrid Models With Multi-Feature Fusion Using Covid-19 Radiography Database”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 27/80 (May 2025), 326-336. https://doi.org/10.21205/deufmd.2025278020.
JAMA Yaşar Çıklaçandır FG, Ulutagay G. Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database. DEUFMD. 2025;27:326–336.
MLA Yaşar Çıklaçandır, Fatma Günseli and Gözde Ulutagay. “Comparison of Hybrid Models With Multi-Feature Fusion Using Covid-19 Radiography Database”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 27, no. 80, 2025, pp. 326-3, doi:10.21205/deufmd.2025278020.
Vancouver Yaşar Çıklaçandır FG, Ulutagay G. Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database. DEUFMD. 2025;27(80):326-3.
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