Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Nallamothu Sri Kavya; Thotapalli Shilpa; N Veeranjaneyulu; D Divya Priya

doi:10.1016/j.matpr.2022.05.199

Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Mater Today Proc. 2022:64:737-743. doi: 10.1016/j.matpr.2022.05.199. Epub 2022 May 19.

Authors

Nallamothu Sri Kavya¹, Thotapalli Shilpa¹, N Veeranjaneyulu¹, D Divya Priya²

Affiliations

¹ Department of IT, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India.
² Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India.

Abstract

With the current COVID19 pandemic, we have to weigh human life, prosperity, and value, while implicitly acknowledging that controlling case spread and mortality is a challenge. Identifying COVID19-infected patients and disconnecting them to avoid COVID transmission is one of the most difficult tasks for clinicians. As a result, figuring out who infected with covid19 is crucial. COVID19 is identified using a 4-6-hour reverse transcription-polymerase chain reaction (RT-PCR). Another way to detect Coronavirus early in the disease process is by using chest X-rays (CXR).We extracted characteristics from chest X-ray images using VGG16 and ResNet50 deep learning algorithms, then classified them into three groups: viral pneumonia, normal, and COVID19. We ran 15,153 images through the models to see how accurate they were in real-world situations. For detecting COVID19 cases, the VGG16 model has an average accuracy of 89.34 %, whereas ResNet50 has an accuracy of 91.39 %. When utilizing deep learning to identify COVID19, however, a larger dataset is necessary. It has the desired effect of detecting situations accurately.

Keywords: COVID19; Chest X-rays; Deep Learning; Pneumonia; ResNet50; VGG16.

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