Enhanced COVID-19 Diagnosis from Chest X-Rays Using Transfer Learning with VGG16

Journal: Journal of Computer Science and Engineering Research (JCSER), Volume:1, Issue:1, Pages: 8-13 Download pdf

Authors: Ennaceur Leila, Hedi Sakli, Mohamed Yahia

Date: 9-2024

Abstract: This study explores the application of a deep learning model based on VGG16 for classifying medical images into three categories: normal, COVID-19, and pneumonia. Leveraging the power of transfer learning, the pre-trained VGG16 model was fine-tuned on a custom dataset to achieve high accuracy in image classification. The model achieved a training accuracy of 98% and a validation accuracy of 96%, with a corresponding training loss of 0.03 and a validation loss of 0.12. These results indicate the model's strong ability to learn from the training data and generalize to unseen validation data. Key techniques such as ReduceLROnPlateau and Early Stopping were employed to optimize the training process by automatically adjusting the learning rate and preventing overfitting. Despite the promising results, limitations such as the size of the dataset and the computational resources required for training were identified. Future work may focus on data augmentation and experimenting with more advanced neural network architectures to further enhance model performance 

Keywords: Medical Image Classification, COVID-19 Detection, Transfer Learning, CNN, VGG16 

References:

[1] Johns Hopkins University. (2024). COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE), https://coronavirus.jhu.edu/map.html

[2] Jacobi A., Chung M., Bernheim A., & Eber, “Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review”. Clinical Imaging, 64, 35-42. https://doi.org/10.1016/j.clinimag.2020.04.001 

[3] Keelin Murphy, Henk Smits, Arnoud J G Knoops, Michael B J M Korst, Tijs Samson, Ernst T Scholten, Steven Schalekamp, Cornelia M Schaefer-Prokop, Rick H H M Philipsen, Annet Meijers , Jaime Melendez, Bram van Ginneken, Matthieu Rutten, “COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System”, Published Online: May 8,  2020.    https://doi.org/10.1148/radiol.2020201874

[4] 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(2), 635-640. https://doi.org/10.1007/s13246-020-00865-4

[5] Jacobi, A., Chung, M., Bernheim, A., & Eber, C. (2020), “Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review”. Clinical Imaging, 64, 35-42. https://doi.org/10.1016/j.clinimag.2020.04.001

[6] Murphy, K., Smits, H., Knoops, A. J. G., Korst, M. B. J. M., Samson, T., Scholten, E. T., ... & Prokop, M. (2020), “COVID-19 on chest radiographs: A multireader evaluation of radiologist performance”. Radiology, 296(3), E166-E175. https://doi.org/10.1148/radiol.2020201233

[7] 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(2), 635-640. https://doi.org/10.1007/s13246-020-00865-4

[8] 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 Radiography Images”. Scientific Reports, 10(1), 19549. https://doi.org/10.1038/s41598-020-76550-z

[9] Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., ... & Summers, R. M. (2016), “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning”. IEEE Transactions on Medical Imaging, 35(5), 1285-1298. https://doi.org/10.1109/TMI.2016.2528162

[10] 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(3), 1207-1220. https://doi.org/10.1007/s10044-021-00984-y

[11] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, “TensorFlow: Large- scale machine learning on heterogeneous systems”, March 2016. Available from: whitepaper2015.pdf (tensorflow.org)

[12] Tawsifur Rahman (Owner), Dr. Muhammad Chowdhury (Editor), Amith Khandakar (Editor), “COVID-19 Radiography Database”, available from: COVID-19 Radiography Database (kaggle.com). 

 [13] 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, 121, 103792. https://doi.org/10.1016/j.compbiomed.2020.103792

[14] 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. https://doi.org/10.1016/j.cmpb.2020.105581

[15] Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., ... & Wang, G. (2020). “Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography”. Cell, 181(6), 1423-1433. https://doi.org/10.1016/j.cell.2020.04.045

[16] 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. https://doi.org/10.48550/arXiv.2003.11055

[17] Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2020). A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167, 108288. https://doi.org/10.1016/j.measurement.2020.108288 ²           

[18] Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. International Journal of Mathematical, Engineering and Management Sciences, 5(4), 643-651. https://doi.org/10.20944/preprints202003.0300.v1 

[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 radiography images. Scientific Reports, 10(1), 19549. https://doi.org/10.1038/s41598-020-76550-z

[20] Rahimzadeh, M., & Attar, A. (2020). A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 19, 100360. https://doi.org/10.1016/j.imu.2020.100360

[21] Chandra, T. B., Verma, K., Singh, B. K., Jain, D., & Netam, S. S. (2020). Automatic detection of tuberculosis-related abnormalities in chest X-ray images using hierarchical feature extraction scheme. Expert Systems with Applications, 158, 113567. https://doi.org/10.1016/j.eswa.2020.113514