Detection of Pneumonia in COVID-19 Patients Using X-ray Images

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Year : November 17, 2023 | Volume : 01 | Issue : 01 | Page : 13-23

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    S.S. Kiran, K Gurucharan, M. Rajan Babu, S. Durgamadhuri, M. Swathi, M. Sravanthi

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  1. Assistant Professor, Assistant Professor, Professor, Assistant Professor, Student, Student, Lendi Institute of Engineering and Technology, Lendi Institute of Engineering and Technology, Lendi Institute of Engineering and Technology, Lendi Institute of Engineering and Technology, Lendi Institute of Engineering and Technology, Trainee Software Engineer, Prolifics, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Telangana, India, India, India, India, India, India

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Abstract

nThis study explores the use of chest X-ray image analysis and deep learning methods to identify pneumonia in COVID-19 patients. Due to the pandemic, Proper as well as immediate examination of COVID-19 is now essential for patient care and disease control. This study proposes a novel approach that uses convolutional neural networks (CNNs) to automatically predict pneumonia in COVID-19 patients using chest X-ray images. In this study, an X-ray of the chest that have been categorized are one-hot encoded using machine learning techniques like LabelBinarizer and then converted into emphatic form applying the categorical functionality of Python. Following that, a method for identification is created utilizing a range of the deep learning features, including convolutional neural network (CNN), VGG16, average pooling 2D (AP2D), dropout, flatten, dense, and input. The proposed approach was evaluated on a 5000 chest X-ray images in the dataset, obtaining excellent classification accuracy of 94%, sensitivity of 60%, and specificity of 100% for healthy and infections with pneumonia. The results demonstrate The possibility of using deep learning methods with accurately and quickly identify pneumonia in COVID-19 patients, which can enhance the health of patients and contribute to preventing the disease’s spread. The method carefully reduces training loss while also improving accuracy.

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Keywords: COVID-19, SARS-CoV-2, Pneumonia, CNN, One hot encoding, Deep Learning, X-Ray, Bacteria, Viruses, Medical imaging.

n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Radio Frequency Innovations(ijrfi)]

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How to cite this article: S.S. Kiran, K Gurucharan, M. Rajan Babu, S. Durgamadhuri, M. Swathi, M. Sravanthi Detection of Pneumonia in COVID-19 Patients Using X-ray Images ijrfi November 17, 2023; 01:13-23

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How to cite this URL: S.S. Kiran, K Gurucharan, M. Rajan Babu, S. Durgamadhuri, M. Swathi, M. Sravanthi Detection of Pneumonia in COVID-19 Patients Using X-ray Images ijrfi November 17, 2023 {cited November 17, 2023};01:13-23. Available from: https://journals.stmjournals.com/ijrfi/article=November 17, 2023/view=0/

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Regular Issue Subscription Original Research

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Volume 01
Issue 01
Received September 8, 2023
Accepted September 22, 2023
Published November 17, 2023

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