Coronavirus Classification based on Enhanced X-ray Images and Deep Learning
DOI:
https://doi.org/10.11113/mjfas.v19n3.2909Keywords:
Coronavirus, COVID-19, classification, X-ray, deep learningAbstract
In light of the fact that the global pandemic of Coronavirus Disease 2019 (COVID-19) is still having a significant impact on the health of people all over the world, there is a growing need for testing diagnosis and treatment that can be completed quickly. The primary imaging modalities used in the respiratory disease diagnostic process are the Chest X-ray (CXR) and the computed tomography scan. In this context, this paper aims to design a new Convolutional Neural Network (CNN) to diagnose COVID-19 in patients based on CXR images and determine whether they are COVID or healthy. We have tested the performance of our CNN on the COVID-19 Radiography Database with three classes (COVID, Pneumonia, and Normal). Also, we proposed a new enhancement technique to enhance the CXR image using the Laplacian kernel with Delta Function and Contrast-Limited Adaptive Histogram Equalization. The proposed CNN has been trained and tested on 15153 enhanced and original images, COVID (3616), Pneumonia (1345), and Normal (10192). Our enhancement technique increased the performance metrics scores of the proposed CNN. Hence, the proposed method obtained better results than the state-of-the-art methods in accuracy, sensitivity, precision, specificity, and F measure.
References
Kadhim, K. A., Adnan, M. M., Waheed, S. R., & Alkhayyat, A. (2021). WITHDRAWN: Automated high-security license plate recognition system, Materials Today: Proceedings, 2021.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., & Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.
Salim, A. A., Ghoshal, S. K., Shamsudin, M. S., Rosli, M. I., Aziz, M. S., Harun, S. W., ... & Bakhtiar, H. (2021). Absorption, fluorescence and sensing quality of Rose Bengal dye-encapsulated cinnamon nanoparticles. Sensors and Actuators A: Physical, 332, 113055.
Salim, A. A., Bidin, N., Bakhtiar, H., Ghoshal, S. K., Al Azawi, M., & Krishnan, G. (2018, May). Optical and structure characterization of cinnamon nanoparticles synthesized by pulse laser ablation in liquid (PLAL). Journal of Physics: Conference Series, 1027(1), 012002. IOP Publishing.
Salim, A. A., Bakhtiar, H., & Ghoshal, S. K. (2021). Improved fluorescence quantum yield of nanosecond pulse laser ablation wavelength controlled cinnamon nanostructures grown in ethylene glycol medium. Optik, 244, 167575..
Adnan, M. M., Rahim, M. S. M., Al-Jawaheri, K., Ali, M. H., Waheed, S. R., & Radie, A. H. (2020, September). A survey and analysis on image annotation. In 2020 3rd International Conference on Engineering Technology and its Applications (IICETA) (pp. 203-208). IEEE.
Waheed, S. R., Suaib, N. M., Rahim, M. S. M., Adnan, M. M., & Salim, A. A. (2021, April). Deep Learning Algorithms-based Object Detection and Localization Revisited. In Journal of Physics: Conference Series, 1892(1), 012001. IOP Publishing.
Yeasmin, N., Mahbub, N. I., Baowaly, M. K., Singh, B. C., Alom, Z., Aung, Z., & Azim, M. A. (2022). Analysis and prediction of user sentiment on COVID-19 pandemic using tweets. Big Data and Cognitive Computing, 6(2), 65.
Molina, G., Mendoza, M., Loayza, I., Núñez, C., Araya, M., Castañeda, V., & Solar, M. (2022). a new content-based image retrieval system for SARS-CoV-2 computer-aided diagnosis. Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021) Medical Imaging and Computer-Aided Diagnosis (pp. 316-324). Springer Singapore.
Hertel, R., & Benlamri, R. (2021). COV-SNET: A deep learning model for X-ray-based COVID-19 classification. Informatics in Medicine Unlocked, 24, 100620.
Hertel, R., & Benlamri, R. (2022). A deep learning segmentation-classification pipeline for x-ray-based covid-19 diagnosis. Biomedical Engineering Advances, 3, 100041.
Mamalakis, M., Swift, A. J., Vorselaars, B., Ray, S., Weeks, S., Ding, W., & Banerjee, A. (2021). DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays. Computerized Medical Imaging and Graphics, 94, 102008.
Ieracitano, C., Mammone, N., Versaci, M., Varone, G., Ali, A. R., Armentano, A. & Morabito, F. C. (2022). A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing, 481, 202-215.
Saad, A., Kamil, I. S., Alsayat, A., & Elaraby, A. (2022). Classification COVID-19 based on enhancement X-Ray images and low complexity model. Computers, Materials and Continua, 561-576.
Shankar, K., & Perumal, E. (2021). A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images. Complex & Intelligent Systems, 7(3), 1277-1293.
T. R. D. M. C. A. Khandakar. COVID-19 Radiography Database [Online] Available: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.
Ali, A. H., & Najjar, F. H. (2018, September). Integrating the kernel method to autonomous learning multi-model systems for online data. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-5). IEEE.
Salim, A. A., Ghoshal, S. K., Danmallam, I. M., Sazali, E. S., Krishnan, G., Aziz, M. S., & Bakhtiar, H. (2021, April). Distinct optical response of colloidal gold-cinnamon nanocomposites: Role of pH sensitization. In Journal of Physics: Conference Series, 1892(1), 012039. IOP Publishing.
Chang, Y., Jung, C., Ke, P., Song, H., & Hwang, J. (2018). Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access, 6, 11782-11792.
Luque, A., Carrasco, A., Martín, A., & de Las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216-231.
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21, 1-13.
Waheed, S. R., Rahim, M. S. M., Suaib, N. M., & Salim, A. A. (2023). CNN deep learning-based image to vector depiction. Multimedia Tools and Applications, 1-20.
Khudhair, K. T., Kadhim, O. N., Najjar, F. H., Abedi, F., Jamaluddin, A. N., & Al-Kharsan, I. H. (2022, May). Soft Edge Detection by Mamdani Fuzzy Inference of Color Image. 2022 5th International Conference on Engineering Technology and its Applications (IICETA) (pp. 379-383). IEEE.
Najjar, F. H., Khudhair, K. T., Khaleq, A. H. A., Kadhim, O. N., Abedi, F., & Al-Kharsan, I. H. (2022, May). Histogram features extraction for edge detection approach. 2022 5th International Conference on Engineering Technology and its Applications (IICETA) (pp. 373-378). IEEE.
Bhattacharyya, A., Bhaik, D., Kumar, S., Thakur, P., Sharma, R., & Pachori, R. B. (2022). A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images. Biomedical Signal Processing and Control, 71, 103182.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 MUHAMMAD SAFWAN ABD AZIZ, Fallah H. Najjar, Safa Riyadh Waheed, Duha Amer Mahdi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
