Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images

M. D. Kamrul Hasan, Sakil Ahmed, Z. M. Ekram Abdullah, Mohammad Monirujjaman Khan, Divya Anand, Aman Singh, Mohammad AlZain and Mehedi Masud

Mathematical Problems in Engineering, 2021, vol. 2021, 1-8

Abstract:

The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python’s to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9929274

DOI: 10.1155/2021/9929274

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