Deep learning techniques to identify and classify Covid-19 abnormalities on chest X-ray images

Elhanashi, Abdussalam and Lowe, Duncan and Saponara, Sergio and Moshfeghi, Yashar; Kehtarnavaz, Nasser and Carlsohn, Matthias F., eds. (2022) Deep learning techniques to identify and classify Covid-19 abnormalities on chest X-ray images. In: Real-Time Image Processing and Deep Learning 2022. Proceedings of SPIE - The International Society for Optical Engineering . SPIE, Virtual, Online. ISBN 9781510650800 (https://doi.org/10.1117/12.2618762)

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Abstract

The new coronavirus disease (COVID-19) comprises the public health systems around the world. The number of infected people and deaths are escalating day-to-day, which puts enormous pressure on healthcare systems. COVID-19 symptoms include fatigue, cough, and fever. These symptoms are also diagnosed for other pneumonia, which creates complications in identifying COVID-19, especially throughout the influenza season. The rise of the COVID-19 pandemic among individuals has made it essential to improve medical image screening of this pneumonia. Rapid identification is a necessary step to stop the spread of this virus and plays a vital role in early detection. With this as a motivator, we applied deep learning techniques to diagnose the coronavirus using chest X-ray images and to implement a robust AI application to classify COVID-19 pneumonia from non-COVID-19 for the respiratory system in these images. This paper proposes different deep learning algorithms, including classification and segmentation methods. By taking advantage of convolutional neural network models, we exploited different pre-trained deep learning models such as (ResNet50, ResNet101, VGG-19, and U-Net architectures) to extract features from chest X-ray images. Four datasets of chest X-ray images have been employed to assess the performance of the proposed methods. These datasets have been split into 80% for training and 20% for validation of the architectures. The experimental results showed an overall accuracy of 99.42% for the classification and 93% for segmentation approaches. The proposed approaches can help radiologists and medical specialists to identify the insights of infected regions for the respiratory system in the early stages.

ORCID iDs

Elhanashi, Abdussalam, Lowe, Duncan, Saponara, Sergio and Moshfeghi, Yashar ORCID logoORCID: https://orcid.org/0000-0003-4186-1088; Kehtarnavaz, Nasser and Carlsohn, Matthias F.
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