Deep Learning Models for the Detection and Classification of COVID-19 and Associated Lung Diseases Using X-Ray Images

Year 2024, Volume: 4 Issue: 2, 121 - 142, 01.10.2024

Osman Dikmen

Abstract

The COVID-19 pandemic has introduced exceptional challenges to healthcare systems worldwide, underscoring the urgent need for swift and precise diagnostic solutions. In this research, we investigate the performance of various deep learning models, including VGG19, ResNet18, and a ResNet18-based U-Net, as well as a Custom Convolutional Neural Network (CNN) developed in MATLAB, for the classification and segmentation of lung X-ray images. The dataset includes X-ray images from individuals diagnosed with COVID-19, viral pneumonia, lung opacity, and healthy individuals. The dataset was divided into 80% for training and 20% for testing, with data augmentation techniques implemented to enhance the model's effectiveness. The VGG19 model, utilizing transfer learning, demonstrated strong diagnostic capabilities, achieving high accuracy rates for COVID-19, lung opacity, healthy lungs, and viral pneumonia classification, with a test accuracy of 97.5%. ResNet18 was employed for both classification and as part of a hybrid model incorporating a U-Net-inspired decoder for lung disease segmentation. The ResNet18 model achieved competitive accuracy and loss metrics, while the ResNet18-based U-Net model excelled in image segmentation tasks, demonstrating its potential in biomedical image analysis. Additionally, a Customized CNN model was developed using MATLAB for the classification of the four lung conditions. This model showed visual outputs including training-validation loss/accuracy graphs and confusion matrices. Our results indicate that deep learning models, especially when combined with transfer learning and customized architectures, offer a powerful approach to diagnosing COVID-19 and related lung conditions. Future work will focus on refining these models with larger datasets and further experimentation to enhance diagnostic performance across diverse clinical settings.

Keywords

COVID-19, Deep Learning, Medical Image Classification, X-Ray Imaging, Convolutional Neural Networks (CNN).

References

• [1] Punn, N. S., & Agarwal, S. (2021). Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Applied Intelligence, 51(5), 2689-2702.
• [2] 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, 83(12), 36665-36682.
• [3] Govindarajan, S., & Swaminathan, R. (2021). Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks. Applied Intelligence, 51(5), 2764-2775.
• [4] Wang, Y., Kang, H., Liu, X., & Tong, Z. (2020). Combination of RT‐qPCR testing and clinical features for diagnosis of COVID‐19 facilitates management of SARS‐CoV‐2 outbreak. Journal of medical virology, 92(6), 538.
• [5] Adhikari, S. P., Meng, S., Wu, Y. J., Mao, Y. P., Ye, R. X., Wang, Q. Z., ... & Zhou, H. (2020). Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infectious diseases of poverty, 9, 1-12.
• [6] Pan, Y., Li, X., Yang, G., Fan, J., Tang, Y., Zhao, J., ... & Li, Y. (2020). Serological immunochromatographic approach in diagnosis with SARS-CoV-2 infected COVID-19 patients. Journal of Infection, 81(1), e28-e32.
• [7] Spicuzza, L., Montineri, A., Manuele, R., Crimi, C., Pistorio, M. P., Campisi, R., ... & Crimi, N. (2020). Reliability and usefulness of a rapid IgM‐IgG antibody test for the diagnosis of SARS-CoV-2 infection: A preliminary report. The Journal of infection, 81(2), e53.
• [8] Porte, L., Legarraga, P., Vollrath, V., Aguilera, X., Munita, J. M., Araos, R., ... & Weitzel, T. (2020). Evaluation of a novel antigen-based rapid detection test for the diagnosis of SARS-CoV-2 in respiratory samples. International Journal of Infectious Diseases, 99, 328-333.
• [9] 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, 13(1), 131.
• [10] Gupta, K., & Bajaj, V. (2023). Deep learning models-based CT-scan image classification for automated screening of COVID-19. Biomedical Signal Processing and Control, 80, 104268.
• [11] 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, 72, 103326.
• [12] Basu, S., Mitra, S., & Saha, N. (2020, December). Deep learning for screening covid-19 using chest x-ray images. In 2020 IEEE symposium series on computational intelligence (SSCI) (pp. 2521-2527). IEEE.
• [13] Che Azemin, M. Z., Hassan, R., Mohd Tamrin, M. I., & Md Ali, M. A. (2020). COVID‐19 deep learning prediction model using publicly available radiologist‐adjudicated chest x‐ray images as training data: preliminary findings. International Journal of Biomedical Imaging, 2020(1), 8828855.
• [14] Aslan, M. (2022). Derin Öğrenme Tabanlı Otomatik Beyin Tümör Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(1), 399-407.
• [15] Erdoğan, A. M. N., Öztürk, T., & Talo, M. (2022). Yeni bir Evrişimsel Sinir Ağı Modeli Kullanarak Bilgisayarlı Tomografi Görüntülerinden Akciğer Kanseri Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 795-802.
• [16] 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, 196, 105581.
• [17] 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, 24, 1207-1220.
• [18] 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, 635-640.
• [19] 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. Scientific reports, 10(1), 19549.
• [20] Kroft, L. J., van der Velden, L., Girón, I. H., Roelofs, J. J., de Roos, A., & Geleijns, J. (2019). Added value of ultra–low-dose computed tomography, dose equivalent to chest x-ray radiography, for diagnosing chest pathology. Journal of thoracic imaging, 34(3), 179-186.
• [21] Damilakis, J., Adams, J. E., Guglielmi, G., & Link, T. M. (2010). Radiation exposure in X-ray-based imaging techniques used in osteoporosis. European radiology, 20, 2707-2714.
• [22] Su, Q., Kloukinas, C., & Garcez, A. D. A. (2024, June). FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series. In 2024 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
• [23] Fayemiwo, M. A., Olowookere, T. A., Arekete, S. A., Ogunde, A. O., Odim, M. O., Oguntunde, B. O., ... & Kayode, A. A. (2021). Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset. PeerJ Computer Science, 7, e614.
• [24] Cao, Z., Huang, J., He, X., & Zong, Z. (2022). BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images. Knowledge-Based Systems, 258, 110040.
• [25] Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., ... & Li, L. (2020). A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering, 6(10), 1122-1129.
• [26] El Asnaoui, K., & Chawki, Y. (2021). Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of Biomolecular Structure and Dynamics, 39(10), 3615-3626.
• [27] Elzeki, O. M., Shams, M., Sarhan, S., Abd Elfattah, M., & Hassanien, A. E. (2021). COVID-19: a new deep learning computer-aided model for classification. PeerJ Computer Science, 7, e358.
• [28] 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.
• [29] Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., ... & Yang, Y. (2021). Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM transactions on computational biology and bioinformatics, 18(6), 2775-2780..
• [30] Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., ... & Zhao, X. (2020). Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. Journal of X-ray Science and Technology, 28(5), 821-839.