Abstract
The most generally utilized strategy for Covid-19 discovery is a continuous Polymerase Chain Response (RT-PCR). These kits for covid-19 detection are over-priced and they require an estimated time of hours or days to verify the disease. Because of the smaller affectability of the test, it gives higher deceptive outcomes. To detect this problem, X-rays of the chest and CT scan methods are utilized to evaluate Covid-19. Here chest X-Rays are chosen for model development. The reason to choose them is that the majority of clinics have X-ray equipment. The computerized investigation of Coronavirus was carried by using 371 X-ray images gathered from the Kaggle dataset to apply the Convolutional Neural Network algorithm. Some images were of infected patients of the Covid19 and others were of normal people. There was an acceptable prediction accuracy of 97.22%. In light of our discoveries, the model can help in making decisions for early Covid-19 identification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wikipedia contributors. COVID-19. Wikipedia. Accessed July 2021, from https://en.wikipedia.org/wiki/COVID-19
Covid-19 tracker. (n.d.). Retrieved 9 Jan 2022, from https://vac-lshtm.shinyapps.io/ncov_tracker/
WHO Coronavirus (COVID-19) Dashboard. World Health Organization. https://covid19.who.int/
Giri, B., Pandey, S., Shrestha, R., Pokharel, K., Ligler, F.S., Neupane, B.B.: Review of analytical performance of COVID-19 detection methods. Anal. Bioanal. Chem. 413(1), 35–48 (2021). https://doi.org/10.1007/s00216-020-02889-x
New-ai-diagnostic. Debuglies (2020). https://debuglies.com/2020/05/12/new-ai-diagnostic-can-predict-whether-someone-is-likely-to-have-covid-19-based-on-their-symptoms/
Bell, D.J.: COVID-19 | Radiology Reference Article | Radiopaedia.org. Radiopaedia. https://radiopaedia.org/articles/covid-19-4
Kaggle.com. 2022. Chest Xray for covid-19 detection. [online] Available at: https://www.kaggle.com/fusicfenta/chest-xray-for-covid19-detection/. [Accessed 20 Nov 2021]
Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020)
Sun, Y., Xue, B., Zhang, M., Yen, G.G., Lv, J.: Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans. Cybern. 50(9), 3840–3854 (2020)
Skalski, P.: Gentle Dive into Math Behind Convolutional Neural Networks (2019). Online: https://www.towardsdatascience.com/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9. Accessed 5 May
CS 230—Convolutional Neural Networks Cheatsheet. Stanford. https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
Brownlee, J.: Dropout Regularization in Deep Learning Models With Keras. Machine Learning Mastery (2020).https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/
Zhao, W., Fu, H., Luk, W., Yu, T., Wang, S., Feng, B., Yang, G.: F-CNN: An FPGA-based framework for training convolutional neural networks. In 2016 IEEE 27Th International Conference on Application-Specific Systems, Architectures and Processors (ASAP), pp. 107–114. IEEE (2016)
Umer, M., Ashraf, I., Ullah, S., Mehmood, A., Choi, G.S.: COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images. J. Ambient Intell. Human. Comput. 1–13 (2021)
Rahimzadeh, M., Attar, A.: 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. Inf. Med. Unlock. 19, 100360 (2020)
Team, K.: Keras documentation: models API. Keras. https://keras.io/api/models/
Sarin, S.: Exploring data augmentation with keras and tensorflow. Medium (2021). https://towardsdatascience.com/exploring-image-data-augmentation-with-keras-and-tensorflow-a8162d89b844
Shiddieqy, H.A., Hariadi, F.I., Adiono, T.: Implementation of deep-learning based image classification on single board computer. In: 2017 International Symposium on Electronics and Smart Devices (ISESD), pp. 133–137. IEEE (2017)
Panwar, H., Gupta, P.K., Siddiqui, M.K., Morales-Menendez, R., Singh, V.: Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons Fractals 138, 109944 (2020)
Nayak, J., Naik, B., Dinesh, P., Vakula, K., Dash, P. B., Pelusi, D.: Significance of deep learning for Covid-19: state-of-the-art review. Res. Biomed. Eng. 1–24 (2021)
Ghaderzadeh, M., Asadi, F.: Deep learning in the detection and diagnosis of COVID-19 using radiology modalities: a systematic review. J. Healthcare Eng. (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Raj, T., Arya, S. (2023). COVID-19 Detection in X-Rays Using Image Processing CNN Algorithm. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-12419-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12418-1
Online ISBN: 978-3-031-12419-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)