A Novel Multi-Head Attention Framework for COVID-19 Detection: Hybrid Integration of MobileNet and VGG19 with Enhanced Feature Learning

Yıl 2025, Cilt: 40 Sayı: 3, 655 - 670, 26.09.2025

Şafak Kılıç

https://doi.org/10.21605/cukurovaumfd.1653486

Öz

The COVID-19 pandemic has underscored the urgent need for rapid, accurate, and affordable diagnostic tools to complement RT-PCR testing. This study proposes a novel multi-head attention framework that integrates VGG19 and MobileNet for automated COVID-19 detection from chest X-rays. The model employs a hybrid mechanism combining spatial, channel, and self-attention components, enhancing feature representation while preserving efficiency.
Evaluations on 7,132 chest X-ray images across four categories (COVID-19, Normal, Pneumonia, Tuberculosis) demonstrated outstanding performance: 99.0% accuracy, 99.0% macro and weighted F1-scores, with near-perfect class-specific results (100% Tuberculosis, 99.7% COVID-19, 99.5% Normal, 96.0% Pneumonia). Inference time was only 63 ms per image, with a compact 14.8 MB model size.
These results surpass baseline MobileNet and DenseNet121 by 2.63% and 4.32%, respectively. The proposed framework offers reliable rapid screening and differential diagnosis, supported by interpretable attention maps, making it highly suitable for deployment in resource-limited healthcare and point-of-care settings.

Anahtar Kelimeler

COVID-19 Detection , Deep Learning , Multi-Head Attention Mechanism , Medical Image Analysis , VGG19

COVID-19 Tespiti için Yeni Bir Çok Başlı Dikkat Çerçevesi: MobileNet ve VGG19 Tabanlı Geliştirilmiş Özellik Öğrenimi ile Hibrit Entegrasyon

Öz

COVID-19 pandemisi, RT-PCR testlerini destekleyecek hızlı, doğru ve maliyet etkin tanı araçlarına duyulan ihtiyacı ortaya koymuştur. Bu çalışmada, göğüs röntgeni görüntülerinden otomatik COVID-19 tespiti için VGG19 ve MobileNet mimarilerini entegre eden yeni bir çok başlı dikkat çerçevesi önerilmektedir. Model, uzamsal, kanal ve çok başlı öz-dikkat mekanizmalarını birleştirerek özellik çıkarımını güçlendirirken hesaplama verimliliğini korumaktadır.
Yaklaşımımız 7.132 görüntüden oluşan dört sınıflı veri kümesinde test edilmiştir (COVID-19, Normal, Pnömoni, Tüberküloz). Dikkat mekanizmasıyla geliştirilmiş MobileNet %99,0 doğruluk, makro ve ağırlıklı F1 skorları elde etmiştir. Sınıf bazında %100 Tüberküloz, %99,7 COVID-19, %99,5 Normal ve %96,0 Pnömoni doğruluğu kaydedilmiştir. Ayrıca model, 14,8 MB boyutu ve 63 ms çıkarım süresi ile klinik uygulanabilirliğe sahiptir.
Sonuçlar, mevcut yöntemlere göre %2,63–%4,32 iyileşme göstermekte olup, modelin güvenilir hızlı tarama ve ayırıcı tanıda etkili olduğunu göstermektedir.

Anahtar Kelimeler

COVID-19 Tespiti , Derin Öğrenme , Çok Başlı Dikkat Mekanizması , Tıbbi Görüntü İşleme , VGG19

Kaynakça

• 1. World Health Organization (2024). Coronavirus disease (Covid-19). https://www.who.int/health-topics/coronavirus, Erişim tarihi: 18 Kasım 2024.
• 2. Wang, L., Lin, Z. Q. & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep. 10, 19549
• 3. Li, C., Dong, D., Li, L., Gong, W., Li, X., Bai, Y., Wang, M., Hu, Z., Zha, Y. & Tian, J. (2020). Classification of severe and critical covid-19 using deep learning and radiomics. IEEE Journal of Biomedical and Health Informatics, 24(12), 3585-3594.
• 4. Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., Weir-McCall, J., Teng, Z., Gkrania-Klotsas, E., Rudd, J.H., Sala, E., Schönlied, C.-B. & Gozaliasi, G. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for covid-19 using chest radiographs and ct scans. Nature Machine Intelligence, 3(3), 199-217.
• 5. Khan, S.H., Sohail, A., Khan, A., Hassan, M., Lee, Y.S., Alam, J., Basit, A. & Zubair, S. (2021). Covid-19 detection in chest x-ray images using deep boosted hybrid learning. Computers in Biology and Medicine, 137, 104816.
• 6. Hryniewska, W., Bombinski, P., Szatkowski, P., Tomaszewska, P., Przelaskowski, A. & Biecek, P. (2021). Checklist for responsible deep learning modeling of medical images based on covid-19 detection studies. Pattern Recognition, 118, 108035.
• 7. Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B. & Rueckert, D. (2019). Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis, 53, 197-207.
• 8. Zhou, S.K., Greenspan, H., Davatzikos, C., Duncan, J.S., Van Ginneken, B., Madabhushi, A., Prince, J.L., Rueckert, D. & Summers, R.M. (2021). A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE, 109(5), 820-838.
• 9. Wang, L., Lin, Z. Q. & Wong, A. (2021). Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 11(1), 1-12.
• 10. Kılıç, Ş. (2025). Attention-based dual-path deep learning for blood cell image classification using ConvNeXt and swin transformer. Journal of Imaging Informatics in Medicine, 1-19.
• 11. Özüpak, Y. (2024). Evrişimli sinir ağı (ESA) mimarileri ile hücre görüntülerinden sıtmanın tespit edilmesi, Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 39(1), 197-210.
• 12. Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H. & Zhang, W. (2020). Accurate screening of covid-19 using attention-based deep 3d multiple instance learning. IEEE Transactions on Medical Imaging, 39(8), 2584-2594.
• 13. Karthik, R., Menaka, R., Hariharan, M. & Won, D. (2022). Contour-enhanced attention cnn for ct-based covid-19 segmentation. Pattern Recognition, 125, 108538.
• 14. Zhang, Y., Niu, S., Qiu, Z., Wei, Y., Zhao, P., Yao, J., Huang, J., Wu, Q. & Tan, M. (2020). Covid-da: Deep domain adaptation from typical pneumonia to covid-19. arXiv preprint, arXiv:2005.01577.
• 15. Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., Lang, G., Li, Y., Zhao, H., Liu, J., Xu, K., Ruan, L., Sheng, J., Qiu, Y., Wu, W., Liang T. & Li, L. (2020). A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering, 6(10), 1122-1129.
• 16. Park, J., Yu, Y., Han, T., Cho, H., Choi, S., Lee, Y., Park, S. & Yoo, J. (2022). Vision transformer using low-level chest x-ray feature corpus for covid-19 diagnosis and severity quantification. Medical Image Analysis, 75, 102299.
• 17. Singh, D., Kumar, V. & Kaur, M. (2021). Densely connected convolutional networks-based covid-19 screening model, Applied Intelligence, 51, 3044-3051.
• 18. Zhou, S. & Qiu, J. (2021). Enhanced ssd with interactive multi-scale attention features for object detection. Multimedia Tools and Applications, 80, 11539-11556.
• 19. Akter, S., Shamrat, F. J. M., Chakraborty, S., Karim, A. & Azam, S. (2021). Covid-19 detection using deep learning algorithm on chest x-ray images. Biology, 10(11), 1174.
• 20. Liu, G. & Guo, J. (2019). Bidirectional lstm with attention mechanism and convolutional layer for text classification. Neurocomputing, 337, 325-338.
• 21. Wang, Q., Han, T., Qin, Z., Gao, J. & Li, X. (2020). Multitask attention network for lane detection and fitting, IEEE Transactions on Neural Networks and Learning Systems, 33(3), 1066-1078.
• 22. Kılıç, Ş. (2025). HybridVisionNet: An advanced hybrid deep learning framework for automated multi-class ocular disease diagnosis using fundus imaging. Ain Shams Engineering Journal, 16(10), 103594.
• 23. Lin, Z., He, Z., Xie, S., Wang, X., Tan, J., Lu, J. & Tan, B. (2021). AANet: Adaptive attention network for COVID-19 detection from chest X-ray images. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4781-4792.
• 24. Saddique, A., Manan, A., Ali, M., Siddiqui, S. & Rehan, M. (2025). Hybrid deep learning models for multi-class classification of chest X-ray images: Normal, pneumonia, and COVID-19. Spectrum of Engineering Sciences, 3(7), 21-33.
• 25. Wang, X., Wang, S., Zhang, Z., Yin, X., Wang, T. & Li, N. (2023). Cpad-net: Contextual parallel attention and dilated network for liver tumor segmentation. Biomedical Signal Processing and Control, 79, 104258.
• 26. Ghosh, S. & Chatterjee, A. (2023). Automated COVID-19 CT image classification using multi-head channel attention in deep CNN. arXiv preprint, arXiv:2308.00715.
• 27. Zhou, T., Chang, X., Liu, Y., Ye, X., Lu, H. & Hu, F. (2023). COVID-ResNet: COVID-19 recognition based on improved attention ResNet. Electronics, 12(6), 1413.
• 28. Ibrahim, W.R. & Mahmood, M.R. (2023). Classified covid-19 by densenet121-based deep transfer learning from ct-scan images. Science Journal of University of Zakho, 11(4), 571-580.
• 29. Canayaz, M. (2021). C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet. Chaos, Solitons & Fractals, 151, 111310.
• 30. Yang, H., Wang, L., Xu, Y. & Liu, X. (2023). CovidViT: A novel neural network with self-attention mechanism to detect COVID-19 through X-ray images. International Journal of Machine Learning and Cybernetics, 14(3), 973-987.
• 31. Erdem, E. & Aydin, T. (2021). Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach. Journal of Soft Computing and Artificial Intelligence, 2(2), 56-68.
• 32. Lin, Z., He, Z., Yao, R., Wang, X., Liu, T., Deng, Y. & Xie, S. (2022). Deep dual attention network for precise diagnosis of COVID-19 from chest CT images. IEEE Transactions on Artificial Intelligence, 5(1), 104-114.
• 33. Yang, L., Hu, T., Zhang, X., Chen, X., Wu, A. & Chang, J. (2023). Enhanced classification of COVID-19 CT images using CDenseNet with CBAM attention and Swish activation. 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA), 160-165. IEEE.
• 34. Li, J., Wang, Y., Wang, S., Wang, J., Liu, J., Jin, Q. & Sun, L. (2021). Multiscale attention guided network for COVID-19 diagnosis using chest X-ray images. IEEE Journal of Biomedical and Health Informatics, 25(5), 1336-1346.
• 35. Tan, Z., Yu, Y., Meng, J., Liu, S. & Li, W. (2024). Self-supervised learning with self-distillation on COVID-19 medical image classification. Computer Methods and Programs in Biomedicine, 243, 107876.
• 36. Yang, Y., Zhang, L., Ren, L. & Wang, X. (2023). MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation. Computer Methods and Programs in Biomedicine, 230, 107348.
• 37. Sun, Y., Lian, J., Teng, Z., Wei, Z., Tang, Y., Yang, L., Zhou, J., Xu, K., Zhang, W., Liu, H., Chen, R. & Lei, B. (2024). COVID-19 diagnosis based on swin transformer model with demographic information fusion and enhanced multi-head attention mechanism. Expert Systems with Applications, 243, 122805.
• 38. Tian, G., Wang, Z., Wang, C., Chen, J., Liu, G., Xu, H., Li, Y., Zhang, Q., Huang, X., Zhou, M. & Peng, L. (2022). A deep ensemble learning-based automated detection of COVID-19 using lung CT images and vision transformer and ConvNeXt. Frontiers in Microbiology, 13, 1024104.
• 39. Khan, A.R. & Khan, A. (2023). MaxViT-UNet: Multi-axis attention for medical image segmentation. arXiv preprint, arXiv:2305.08396.
• 40. Shao, H., Zeng, Q., Hou, Q. & Yang, J. (2025). Mcanet: Medical image segmentation with multi-scale cross-axis attention. Machine Intelligence Research, 22(3), 437-451.
• 41. Wu, H., Li, N., Zhang, J., Chen, S., Ng, M.K. & Long, J. (2024). Collaborative contrastive learning for hypergraph node classification. Pattern Recognition, 146, 109995.
• 42. Sergio, G.C. & Lee, M. (2021). Stacked debert: All attention in incomplete data for text classification. Neural Networks, 136, 87-96.
• 43. Chen, Z., Lou, K., Liu, Z., Li, Y., Luo, Y. & Zhao, L. (2024). Joint long and short span self-attention network for multi-view classification. Expert Systems with Applications, 235, 121152.
• 44. Singh, P. (2023). Covid-19 chest X-ray dataset. https://www.kaggle.com/datasets/preetviradiya /covid19-radiography-dataset. Access date: 14.09.2025.
• 45. Öter, E. & Doğan, Y. (2024). A comparative study on data balancing methods for alzheimer's disease classification. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 39(2), 489-501.