1
Department of Computer Science, Faculty of Computers and Information, Suez University, Suez 43533, Egypt
(yasser.ramadan@suezuni.edu.eg)
2
Department of Information Systems, Faculty of Computers and Information, Suez University, Suez 43533, Egypt
(a.osman@suezuni.edu.eg)
3
Department of Information Systems, Faculty of Computers and Information, Suez University, Suez 43533, Egypt
(hemased111@gmail.com)
4
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(azaki@jcsis.org)
5
Department of Computer Science, Faculty of Computers and Information, Suez University, Suez 43533, Egypt
(Ahmed.Elshewey@fci.suezuni.edu.eg)
Abstract :
In modern times, a disease known as COVID-19 that is highly contagious is continuing to have a profoundly negative influence on the people of the entire world. The fundamental purpose of the model that has been proposed is to improve its predictive capabilities while also providing an effective model for predicting COVID-19 that has a robust diagnostic. Image scaling and noise reduction are two examples of the types of pre-processing techniques that are used at the very first step. The adoption of picture scaling and median filtering techniques, both of which work to enhance the quality of the input data in preparation for further processing steps, allows this goal to be accomplished. Several distinct data augmentation strategies, like flipping and rotation, are utilized to improve the model's performance on a limited dataset and assist it in better comprehending the differences present in the training data. In this article, we will provide a unique Optimized Architecture for COVID-19 Prediction (OACP) model to classify COVID-19 situations as either positive or negative effectively. Using CXR pictures, this novel method, based on a tunable deep learning technique called DenseNet, may predict the presence of COVID-19-positive patients. Based on the findings, it was determined that the proposed model utilized achieved better outcomes, with an accuracy of 98%.
Keywords :
COVID-19; Pre-processing Optimized DenseNet , CNN; Data augmentation , Classification.
References :
[1] Hua, J., Shaw, R.: Coronavirus (COVID-19) infodemic and emerging issues through a data lens: the case of China. Int. J. Environ. Res. Public Health 17(7), 2309 (2020).
[2] Chhikara, B.S., Rathi, B., Singh, J., Poonam, F.N.U.: Coronavirus SARS-CoV-2 disease COVID-19: infection, prevention and clinical advances of the prospective chemical drug therapeutics. Chem. Biol. Lett. 7(1), 63–72 (2020)
[3] Saif, L.J., Jung, K.: Comparative pathogenesis of bovine and porcine respiratory coronaviruses in the animal host species and SARS-CoV-2 in humans. J. Clin. Microbiol. 58(8), e01355-e1420
(2020)
[4] Clementini, M., Raspini, M., Barbato, L., Bernardelli, F., Braga, G., Di Gioia, C., Cairo, F.: Aerosol transmission for SARSCoV-2 in the dental practice. A review by SIdP COVID-19 taskforce. Oral Dis. 28, 852–857 (2020)
[5] Guo, J.W., Radlof, C.L., Wawrzynski, S.E., Cloyes, K.G.: Mining twitter to explore the emergence of COVID-19 symptoms. Public Health Nurs. 37(6), 934–940 (2020)
[6] Carter, L.J., Garner, L.V., Smoot, J.W., Li, Y., Zhou, Q., Saveson, C.J., Liu, C.: Assay techniques and test development for COVID-19 diagnosis. 6, 591–605 (2020)
[7] Lu, Y., Li, L., Ren, S., Liu, X., Zhang, L., Li, W., Yu, H.: Comparison of the diagnostic efcacy between two PCR test kits for SARS-CoV-2 nucleic acid detection. J. Clin. Lab. Anal. 34(10), e23554 (2020)
[8] Bièche, I., Noguès, C., Paradis, V., Olivi, M., Bedossa, P., Lidereau, R., Vidaud, M.: Quantitation of hTERT gene expression in sporadic breast tumors with a real-time reverse transcription-polymerase chain reaction assay. Clin. Cancer Res. 6(2),
452–459 (2000).
[9] Militante, S.V., Dionisio, N.V., Sibbaluca, B.G.: Pneumonia and COVID-19 detection using convolutional neural networks. In: 2020 Third International Conference on Vocational Education
and Electrical Engineering (ICVEE) IEEE, pp. 1–6. (2020)
[10] Tang, Y.B., Tang, Y.X., Xiao, J., Summers, R.M.: Xlsor: A robust and accurate lung segmentor on chest x-rays using crisscross attention and customized radiorealistic abnormalities generation. In: International Conference on Medical Imaging with
Deep Learning, pp. 457–467. PMLR. (2019)
[11] Awadalla, B.A.E., Khalid, K.A.: The important positioning techniques for the investigation of chest diseases-practical approach. 1-62, (2000).
[12] Tabik, S., Gómez-Ríos, A., Martín-Rodríguez, J.L., Sevillano García, I., Rey-Area, M., Charte, D., Herrera, F.: COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images. IEEE J. Biomed. Health Inform.
24(12), 3595–3605 (2020)
[13] Bouxsein, M.L., Boyd, S.K., Christiansen, B.A., Guldberg, R.E., Jepsen, K.J., Müller, R.: Guidelines for assessment of bone microstructure in rodents using micro–computed tomography. J. Bone Miner. Res. 25(7), 1468–1486 (2010)
[14] Solovyev, R., Melekhov, I., Lesonen, T., Vaattovaara, E., Tervonen, O.,Tiulpin, A. Bayesian feature pyramid networks for automatic multi-label segmentation of chest X-rays and assessment of cardio-thoratic ratio. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 117–130. (2020)
[15] Cohen, J., Normile, D.: New SARS-like virus in China triggers alarm. Scienece. 367, 234–235 (2020)
[16] Balyen, L., Peto, T.: Promising artifcial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac. J. Ophthalmol. 8(3), 264–272 (2019)
[17] Mair, C., Kadoda, G., Lefey, M., Phalp, K., Schofeld, C., Shepperd, M., Webster, S.: An investigation of machine learning based prediction systems. J. Syst. Softw. 53(1), 23–29 (2000)
[18] Ibrahim, I., Abdulazeez, A.: The role of machine learning algorithms for diagnosing diseases. J. Appl. Sci. Technol. Trends 2(01), 10–19 (2021)
[19] Fong, S., Deb, S., Yang, X.S.: How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics. In: Progress in intelligent computing techniques: theory practice and applications, pp. 3–25. Springer, Singapore (2018)
[20] Firouzi, F., Farahani, B., Daneshmand, M., Grise, K., Song, J., Saracco, R., Luo, A.: Harnessing the power of smart and connected health to tackle COVID-19: Iot, AI, robotics, and blockchain for a better world. IEEE Internet Things J. 8(16), 12826–12846 (2021)
[21] Asada, K., Komatsu, M., Shimoyama, R., Takasawa, K., Shinkai, N., Sakai, A., Hamamoto, R.: Application of artifcial intelligence in COVID-19 diagnosis and therapeutics. J. Pers. Med. 11(9), 886 (2021)
[22] Bhattacharyya, A., Bhaik, D., Kumar, S., Thakur, P., Sharma, R., Pachori, R.B.: A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images. Biomed. Signal Process. Control 71, 103182 (2022)
[23] Shastri, S., Kansal, I., Kumar, S., Singh, K., Popli, R., Mansotra, V.: CheXImageNet: a novel architecture for accurate classifcation of COVID-19 with chest x-ray digital images using deep convolutional neural networks. Health Technol. 12, 1–12 (2022)
[24] Khan, E., Rehman, M.Z.U., Ahmed, F., Alfouzan, F.A., Alzahrani, N.M., Ahmad, J.: Chest X-ray classifcation for the detection of COVID-19 using deep learning techniques. Sensors 22(3), 1211 (2022)
[25] Alshazly, H., Linse, C., Barth, E., Martinetz, T.: Explainable COVID-19 detection using chest CT scans and deep learning. Sensors 21(2), 455 (2021)
[26] Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Mirjalili, S., Khan, M.K.:Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. InMultimodal image exploitation and learning. Int. Soc. Optics Photonics 11734, 99–110 (2021)
[27] Ismael, A.M., Şengür, A.: Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst. Appl. 164, 114054 (2021)
[28] Chen, J.I.Z.: Design of accurate classifcation of COVID-19 disease in X-ray images using deep learning approach. Journal of ISMAC 3(2), 132–148 (2021)
[29] Hussain, E., Hasan, M., Rahman, M.A., Lee, I., Tamanna, T., Parvez, M.Z.: CoroDet: a deep learning based classifcation for COVID-19 detection using chest X-ray images. Chaos Solitons
Fractals 142, 110495 (2021)
[30] Benmalek, E., Elmhamdi, J., Jilbab, A.: Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis. Biomed. Eng. Adv. 1, 100003 (2021)
[31] covid-chestxray-dataset https://github.com/ieee8023/covid-chestxray-dataset
[32] chest-xray-pneumonia https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
[33] Kermany, D., Zhang, K., Goldbaum, M.: Labeled optical coherence tomography (OCT) and chest X-ray images for classification. Mendeley Data V2 (2018).
[34] Fouad, Y.; Osman, A.M.; Hassan, S.A.; El-Bakry, H.M.; Elshewey, A.M. Adaptive Visual Sentiment Prediction Model Based on Event Concepts and Object Detection Techniques in Social Media. Int. J. Adv. Comput. Sci. Appl. 14(7), 2023
| Style | # |
|---|---|
| MLA | Yasser Fouad, Ahmed M. Osman, Ibrahim E. Abdelmaged, Ahmed Mohamed Zaki, Ahmed M. Elshewey. "An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images." Journal of Artificial Intelligence and Metaheuristics, Vol. 6, No. 1, 2023 ,PP. 35-47 (Doi : https://doi.org/10.54216/JAIM.060104) |
| APA | Yasser Fouad, Ahmed M. Osman, Ibrahim E. Abdelmaged, Ahmed Mohamed Zaki, Ahmed M. Elshewey. (2023). An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images. Journal of Journal of Artificial Intelligence and Metaheuristics, 6 ( 1 ), 35-47 (Doi : https://doi.org/10.54216/JAIM.060104) |
| Chicago | Yasser Fouad, Ahmed M. Osman, Ibrahim E. Abdelmaged, Ahmed Mohamed Zaki, Ahmed M. Elshewey. "An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images." Journal of Journal of Artificial Intelligence and Metaheuristics, 6 no. 1 (2023): 35-47 (Doi : https://doi.org/10.54216/JAIM.060104) |
| Harvard | Yasser Fouad, Ahmed M. Osman, Ibrahim E. Abdelmaged, Ahmed Mohamed Zaki, Ahmed M. Elshewey. (2023). An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images. Journal of Journal of Artificial Intelligence and Metaheuristics, 6 ( 1 ), 35-47 (Doi : https://doi.org/10.54216/JAIM.060104) |
| Vancouver | Yasser Fouad, Ahmed M. Osman, Ibrahim E. Abdelmaged, Ahmed Mohamed Zaki, Ahmed M. Elshewey. An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 6 ( 1 ): 35-47 (Doi : https://doi.org/10.54216/JAIM.060104) |
| IEEE | Yasser Fouad, Ahmed M. Osman, Ibrahim E. Abdelmaged, Ahmed Mohamed Zaki, Ahmed M. Elshewey, An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 6 , No. 1 , (2023) : 35-47 (Doi : https://doi.org/10.54216/JAIM.060104) |