Diagnosis of COVID-19 in X-ray Images using Deep Neural Networks
DOI:
https://doi.org/10.54392/irjmt24318Keywords:
Chest X-rays, COVID-19, Xception model, Segmentation models, U-NetAbstract
The global COVID-19 pandemic has presented unprecedented challenges, notably the limited availability of test kits, hindering timely and accurate disease diagnosis. Rapid identification of pneumonia, a common COVID-19 consequence, is crucial for effective management. This study focuses on COVID-19 classification from Chest X-ray images, employing an innovative approach: adapting the Xception model into a U-Net architecture via the Segmentation_Models package. Leveraging deep learning and image segmentation, the U-Net architecture, a CNN variant, proves ideal for this task, particularly after tailoring its output layer for classification. By utilizing the Xception model, we aim to enhance COVID-19 classification accuracy and efficiency. The results demonstrate promising autonomous identification of COVID-19 cases, offering valuable support to healthcare professionals. The fusion of medical imaging data with advanced neural network architectures highlights avenues for improving diagnostic accuracy during the pandemic. Notably, precision, recall, and F1 scores for each class are reported: Normal (Precision = 0.98, Recall = 0.9608, F1 Score = 0.9704), Pneumonia (Precision = 0.9579, Recall = 0.9579, F1 Score = 0.9579), and COVID-19 (Precision = 0.96, Recall = 0.9796, F1 Score = 0.9698). These findings underscore the effectiveness of our approach in accurately classifying COVID-19 cases from chest X-ray images, offering promising avenues for enhancing diagnostic capabilities during the pandemic.
References
F. Wu, S. Zhao, B. Yu, Y.M. Chen, W. Wang, Z.G. Song, Y. Hu, Z.W. Tao, J.H. Tian, Y.Y. Pei, M.L.A. Yuan, A new coronavirus associated with human respiratory disease in China. Nature, 579(7798), (2020) 265-269. https://doi.org/10.1038/s41586-020-2008-3
R. Mehrrotraa, M.A. Ansari, R. Agrawal, P. Tripathi, M.B.B. Heyat, M. Al-Sarem, A.Y.M. Muaad, W.A.E. Nagmeldin, A. Abdelmaboud, F. Saeed, Ensembling of efficient deep convolutional networks and machine learning algorithms for resource effective detection of tuberculosis using thoracic (chest) radiography. IEEE Access, 10, (2022) 85442-85458. https://doi.org/10.1109/ACCESS.2022.3194152
Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, J. Liang, Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, Proceedings 4, (2018) 3-11. https://doi.org/10.1007/978-3-030-00889-5_1
N. Siddique, S. Paheding, C.P. Elkin, V. Devabhaktuni, U-net and its variants for medical image segmentation: A review of theory and applications. IEEE Access, 9, (2021) 82031-82057. https://doi.org/10.1109/ACCESS.2021.3086020
D. Gupta, Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras, arXiv preprint arXiv:2307.13215 (2023). https://doi.org/10.48550/arXiv.2307.13215
A.P. Apte, A. Iyer, M. Thor, R. Pandya, R. Haq, J. Jiang, E. LoCastro, A. Shukla-Dave, N. Sasankan, Y. Xiao, Y.C. Hu, Library of deep-learning image segmentation and outcomes model-implementations. Physica Medica, 73, (2020) 190-196. https://doi.org/10.1016/j.ejmp.2020.04.011
O. Ronneberger, P. Fischer, T. Brox, Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, proceedings, part III 18, (2015) 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
A.T. Meem, M.M. Khan, M. Masud, S. Aljahdali, Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN. Computer Systems Science & Engineering, 41(3), (2022) 121668. https://doi.org/10.32604/csse.2022.021563
M. Diwakar, P. Singh, A. Shankar, Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain. Biomedical Signal Processing and Control, 68, (2021) 102788. https://doi.org/10.1016/j.bspc.2021.102788
M. Das, D. Gupta, A. Bakde, An end-to-end content-aware generative adversarial network-based method for multimodal medical image fusion. In Data Analytics for Intelligent Systems: Techniques and solutions. IOP Publishing, Bristol, UK, (2024). https://doi.org/10.1088/978-0-7503-5417-2ch7
Y. Jie, Y. Xu, X. Li, H. Tan, TSJNet: A Multi-modality Target and Semantic Awareness Joint-driven Image Fusion Network. arXiv preprint arXiv:2402.01212 (2024). https://doi.org/10.48550/arXiv.2402.01212
E. Irmak, Implementation of convolutional neural network approach for COVID-19 disease detection. Physiological Genomics, 52(12), (2020) 590-601. https://doi.org/10.1152/physiolgenomics.00084.2020
F. Shan, Y. Gao, J. Wang, W. Shi, N. Shi, M. Han, Z. Xue, D. Shen, Y. Shi, Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction. Medical physics, 48(4), (2021) 1633-1645. https://doi.org/10.1002/mp.14609
A. Amyar, R. Modzelewski, H. Li, S. Ruan, Multi-task deep learning-based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Computers in Biology and Medicine, 126, (2020) 104037. https://doi.org/10.1016/j.compbiomed.2020.104037
J. Zhu, B. Shen, A. Abbasi, M. Hoshmand-Kochi, H. Li, T.Q. Duong, Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PloS one, 15(7), (2020) e0236621. https://doi.org/10.1371/journal.pone.0236621
K. He, W. Zhao, X. Xie, W. Ji, M. Liu, Z. Tang, Y. Shi, F. Shi, Y. Gao, J. Liu, J. Zhang, J. Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images. Pattern recognition, 113, (2021) 107828. https://doi.org/10.1016/j.patcog.2021.107828
M.M. Mijwil, Deep Convolutional Neural Network Architecture to Detect COVID-19 from Chest X-Ray Images. Iraqi Journal of Science, (2023) 2561-2574. https://doi.org/10.24996/ijs.2023.64.5.38
A.M.U.D. Khanday, Q.R. Khan, S.T. Rabani, Ensemble Approach for Detecting COVID-19 Propaganda on Online Social Networks. Iraqi Journal of Science, 63(10), (2022) 4488-4498. https://doi.org/10.24996/ijs.2022.63.10.33
Z. Tang, W. Zhao, X. Xie, Z. Zhong, F. Shi, J. Liu, D. Shen, Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv preprint arXiv:2003.11988 (2020). https://doi.org/10.48550/arXiv.2003.11988
L.S. Xiao, P. Li, F. Sun, Y. Zhang, C. Xu, H. Zhu, F.Q. Cai, Y.L. He, Y.L., W.F. Zhang, S.C. Ma, C. Hu, Development and validation of a deep learning-based model using computed tomography imaging for predicting disease severity of coronavirus disease 2019. Frontiers in Bioengineering and Biotechnology, 8, (2020) 898. https://doi.org/10.3389/fbioe.2020.00898
Z. Yu, X. Li, H. Sun, J. Wang, T. Zhao, H. Chen, Y. Ma, S. Zhu, Z. Xie, Rapid identification of COVID-19 severity in CT scans through classification of deep features. Biomedical Engineering Online, 19, (2020) 1-13. https://doi.org/10.1186/s12938-020-00807-x
A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24, (2021) 1207-1220. https://doi.org/10.1007/s10044-021-00984-y
M.M. Salih, M. Ahmed, B. Al-Bander, K.F. Hasan, M.L. Shuwandy, Z. Al-Qaysi, Benchmarking Framework for COVID-19 Classification Machine Learning Method Based on Fuzzy Decision by Opinion Score Method. Iraqi Journal of Science, 64(2), (2023) 922-943. https://doi.org/10.24996/ijs.2023.64.2.36
H. Nasiri, S.A. Alavi, A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray images. Computational intelligence and Neuroscience, 2022, (2022). https://doi.org/10.1155/2022/4694567
P. Patil, V. Narawade, Radiology Image Data Augmentation and Image Enhancement in Respiratory Disease Infection Detection Using Machine Learning Approach. International Research Journal of Multidisciplinary Technovation, 6(2), (2024) 133-155. https://doi.org/10.54392/irjmt24211
D. Kermany, K. Zhang, M. Goldbaum, Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data, 2(2), (2018) 651. https://doi.org/10.17632/rscbjbr9sj.2
T. Mahmud, M.A. Rahman, S.A. Fattah 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, (2020) 103869. https://doi.org/10.1016/j.compbiomed.2020.103869
I. Culjak, D. Abram, T. Pribanic, H. Dzapo, M. Cifrek, A brief introduction to OpenCV. in 2012 proceedings of the 35th international convention MIPRO. IEEE, (2012) 1725-1730.
P. Sane, R. Agrawal, Pixel normalization from numeric data as input to neural networks: For machine learning and image processing. In 2017 international conference on wireless communications, signal processing and networking (WiSPNET), IEEE, (2017) 2221-2225. https://doi.org/10.1109/WiSPNET.2017.8300154
L. Jie, C. Jiahao, Z. Xueqin, Z. Yue, L. Jiajun, One-hot encoding and convolutional neural network-based anomaly detection. Journal of Tsinghua University (Science and Technology), 59(7), (2019) 523-529.
P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, A. Haworth, A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology, 65(5), (2021) 545-563. https://doi.org/10.1111/1754-9485.13261
F. Chollet, Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, (2017) 1251-1258. https://doi.org/10.1109/CVPR.2017.195
T. Ridnik, E. Ben-Baruch, A. Noy, L. Zelnik-Manor, Imagenet-21k pretraining for the masses. arXiv preprint arXiv:2104.10972 (2021). https://doi.org/10.48550/arXiv.2104.10972
D. Wilson, S. Laxminarayan, Handbook of Biomedical Image Analysis: Volume 1: Segmentation Models Part A. Springer Science & Business Media (2006).
D. Müller, F. Kramer, MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning. BMC medical Imaging, 21, (2021) 1-11. https://doi.org/10.1186/s12880-020-00543-7
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Mohammed Akram Younus Alsaati (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
