Digital Image Processing

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Detection of COVID-19 Infection from Chest X-Ray Images using CNN


Affiliations
1 Department of CSE, SJB Institute of Technology, Bangalore, India
2 Department of CSE, SJB Institute of Technology, Bangalore, India
     

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The diagnosis of SARS CoV-2, which is held accountable for corona virus disease, utilizing radiographic images has vital significance for both the sufferer and the Health personnel. Moreover, the nations which are incompetent in acquiring laboratory kits for diagnosis purpose, here it becomes even more crucial. In our model, we target to symbolize the usage of deep neural network for the ability to detect the SARS CoV-2 using radiographic images. Openly attainable radiographic images were utilized in the testing, which requires building of deep neural network and machine learning classifiers. The mean accuracy of 98.50% is attained.

Keywords

Corona Virus, CNN, COVID-19.
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  • "Wang et al ., "Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers" 10.1101/2020.04.13.20063461 April 2020."

  • "Tulin Ozturka, Muhammed Talob, Eylul Azra Yildirimc, Ulas Baran Baloglud, Ozal Yildirime, U. Rajendra Acharya., "Automated detection of COVID-19 cases using deep neural networks with X-ray images" 28 April 2020"

  • "BejoyAbraham and Madhu S.Nair., "Computer-aided detection of COVID-19 from X- ray images using multi-CNN and Bayesnet classifier" 2 September 2020."

  • "Yuan Gao and GUANG YANG., "Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images" June 29, 2020."

  • "Asmaa Abbas & Mohammed M. Abdelhamid., "Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network " 05 September."

  • "Yujin Oh, Sang Joon Park, and Jong Chul Ye., "Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets" 08 May 2020."

  • "Xi Ouyang, Yaozong Gao, Dijia Wu, Qian Wang, and Dinggang Shen., "Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community-Acquired Pneumonia" 18 May 2020."

  • "Xing gang Wang., "A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT" July 30, 2020."

  • "Mangal, A., Kalia, S., Rajgopal, H., Rangarajan, K., Namboodiri, V., Banerjee, S., & Arora, C.. CovidAID: COVID-19 Detection Using Chest X-Ray. rXiv,abs/2004.09803. 2020."

  • " Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., et al.: Chexnet: Radiologist- level pneumonia detection on chest x-rays with deep learning."

  • "C. Huang et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, vol. 395, pp. 497506, Feb. 2020."

  • "Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., Shi, Q., Jin, S., Zhang, L., & You, Z. (2020). COVID- 19 Chest CT Image Segmentation A Deep Convolutional Neural Network Solution. ArXiv, abs/2004.10987."


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  • Detection of COVID-19 Infection from Chest X-Ray Images using CNN

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Authors

Dhruv Rao
Department of CSE, SJB Institute of Technology, Bangalore, India
G. Bindu Priya
Department of CSE, SJB Institute of Technology, Bangalore, India
D. Harshini
Department of CSE, SJB Institute of Technology, Bangalore, India
B. C. Varshini
Department of CSE, SJB Institute of Technology, Bangalore, India
Basamma Umesh Patil
Department of CSE, SJB Institute of Technology, Bangalore, India

Abstract


The diagnosis of SARS CoV-2, which is held accountable for corona virus disease, utilizing radiographic images has vital significance for both the sufferer and the Health personnel. Moreover, the nations which are incompetent in acquiring laboratory kits for diagnosis purpose, here it becomes even more crucial. In our model, we target to symbolize the usage of deep neural network for the ability to detect the SARS CoV-2 using radiographic images. Openly attainable radiographic images were utilized in the testing, which requires building of deep neural network and machine learning classifiers. The mean accuracy of 98.50% is attained.

Keywords


Corona Virus, CNN, COVID-19.

References