Covid-19 and Viral Pneumonia Detection from Chest X-Ray Images Using Pretrained Deep Convolutional Neural Networks
Keywords:
Covid-19, CNN, Chest X-ray, Fine-tuning, PneumoniaAbstract
The use of chest X-ray (CXR) in diagnosing respiratory diseases such as covid-19 and viral pneumonia has gained popularity due to its safe and non-invasive nature. However, interpreting CXR images as it is highly dependent on the expertise and experience of the radiologists may lead to inter-observer variability. A thorough analysis which in most cases takes some time then needs to be performed to get a final correct decision on whether a patient is indicated for a respiratory disease or not. Thus, research on devising more effective and efficient schemes to assist radiologists in the identification of respiratory diseases from CXR images is considered extremely important. In the previous studies, the convolutional neural network (CNN) based models demonstrate their capability to detect respiratory diseases not only they are found faster and reliable but also they are able to give a considerable high classification accuracy. In an attempt to obtain the general CNN based model capable of giving the best classification accuracy, sensitivity, and specificity on respiratory diseases from CXR images, in this paper powerful pretrained deep CNN models namely VGG16, DenseNet121, InceptionV3, Xception, and InceptionResnetV2 are computationally experimented on three different datasets. Dataset 1 is generated by combining images from Covid19 Radiography Database and Chest X-ray COVID-19 Pneumonia Dataset. Dataset 2 is created by combining images from Curated Chest X-ray Image Dataset, COVID-19 Pneumonia Normal Chest X-ray Images, and Chest X-ray COVID-19 Pneumonia Dataset. Meanwhile dataset 3 is taken from COVID-QU-Ex Dataset. State of the art accuracy of the pretrained CNN models is achieved by fine tuning the parameters of the convolutional layers of the base models and followed by feeding the high-level feature maps extracted from each corresponding base model into global average pooling (GAP) layer prior to classifying the respiratory diseases by fully connected layers. The highest average testing accuracy score as high as 99.2% is achieved by the InceptionV3 on multi-class CXR images.
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R. K. Gupta, A. K. Yadav, and K. B. Gupta, "Medical imaging techniques and computer-aided diagnostic approaches for the detection of breast cancer using mammography: A review," J. X-ray Sci. Technol., vol. 27, no. 2, pp. 315-331, 2019.
H. Ali, M. Abdar, M. Ali, and A. Awan, "Inter-observer variability in the interpretation of chest x-rays: A review," Cureus, vol. 12, no. 1, pp. e6633, 2020.
H. Y. Wong et al., "Frequency and distribution of chest radiographic findings in patients positive for COVID-19," Radiology, vol. 296, no. 2, pp. E72-E78, 2020.
E. E. Hemdan, M. A. Shouman, and M. E. Karar, "COVIDX-net: A framework of deep learning classifiers to diagnose COVID-19 in x-ray images," arXiv preprint arXiv:2003.11055, 2020.
P. Rajpurkar et al., "Mura: Large dataset for abnormality detection in musculoskeletal radiographs," arXiv preprint arXiv:1712.06957, 2017.
L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, and Z. Fang, "Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT," Radiology, vol. 296, no. 2, pp. E65-E71, 2020. [Online].Available:https://doi.org/10.1148/radiol.2020200905.
R. Jain, M. Gupta, S. Taneja, and D. J. Hemanth, "Deep learning based detection and analysis of COVID-19 on chest X-ray images," Appl. Intell., vol. 51, pp. 1690-1700, 2020.
N. Sri Kavya, T. Shilpa, N. Veeranjaneyulu, and D. Divya Priya, "Detecting COVID-19 and pneumonia from chest X-ray images using deep convolutional neural networks," Mater. Today Proc., 2022.
M. Farooq, A. Hafeez, and M. Hassan, "A deep learning approach for COVID-19 diagnosis based on chest x-ray images," Expert Syst. Appl., vol. 164, p. 114054, 2021. [Online].Available:https://doi.org/10.1016/j.eswa.2020.114054
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2017, pp. 4700-4708.
C. Szegedy et al., "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2016, pp. 2818-2826.
F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2017, pp. 1251-1258.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-resnet-v2: Improved recognition for smaller networks," in Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2017, pp. 427-436.
Y. Oh, S. Park, and J. C. Ye, "Deep learning COVID-19 features on CXR using limited training data sets," IEEE Trans. Med. Imaging, vol. 39, pp. 2688-2700, 2020.
Y. Brima, M. Atemkeng, S. Tankio Djiokap, J. Ebiele, and F. Tchakounté, "Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images," Diagnostics, vol. 11, p. 1480, 2021.
J. Wang, Y. Peng, H. Xu, Z. Cui; Williams, R.O. The COVID-19 vaccine race: Challenges and opportunities in vaccine formulation. AAPS PharmSciTech 2020, 21, 1–12.
T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Computers in Biology and Medicine, vol. 121, p. 103792, 2020. [Online]. Available: https://doi.org/10.1016/j.compbiomed.2020.103792.
B. N. Narayanan, R. C. Hardie, V. Krishnaraja, C. Karam, and P. Davuluru VS, "Transfer-to-transfer learning approach for computer aided detection of COVID-19 in chest radiographs," AI, vol. 1, pp. 539-557, 2020.
I.D. Apostolopoulos and T.A. Mpesiana, "Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks," Phys. Eng. Sci. Med., vol. 43, pp. 635-640, 2020.
I. U. Khan and N. Aslam, "A deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images," Information, vol. 11, p. 419, 2020.
K. Domantas and S. Clement, "The detection of COVID-19 in chest X-rays using ensemble CNN techniques," J. Med. Syst., vol. 46, no. 2, pp. 18, 2022.
Prof. Parvaneh Basaligheh. (2017). Design and Implementation of High Speed Vedic Multiplier in SPARTAN 3 FPGA Device. International Journal of New Practices in Management and Engineering, 6(01), 14 - 19. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/51
Ramesh, P. V. ., Hrishikesh, J. T. ., & Patil, M. S. . (2023). Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1s), 71–79. https://doi.org/10.17762/ijritcc.v11i1s.6002
Jain, V., Beram, S. M., Talukdar, V., Patil, T., Dhabliya, D., & Gupta, A. (2022). Accuracy enhancement in machine learning during blockchain based transaction classification. Paper presented at the PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, 536-540. doi:10.1109/PDGC56933.2022.10053213 Retrieved from www.scopus.com
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