Abstract
The effectiveness of the treatments applied to patients with COVID-19 in serious and critical condition admitted to intensive care units is a necessary element to draw up the strategies and protocols to follow in each particular case. An automatic index that allows to quantify the degree of affectation produced by the disease in the lungs from X-ray images of the thorax has not been investigated so far.
The work presents a method for estimation of a lung affectation index in chest X-ray images in patients diagnosed with COVID-19 in an advanced stage of the disease. The index is obtained from a method that combines image quality evaluation, digital image processing and deep learning for lung region segmentation. This method is capable of facing the problem of very diffuse borders due to the notable effects that COVID-19 patients in serious or critical condition have. The subsequent step of our proposal consist in the classification of the previously segmented image into two classes (healthy region, affected region) establishing the relationship between the number of pixels of each class. The results achieved in the experiments on images of healthy and affected by COVID-19 patients showed high values of sensitivity and specificity.
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Acknowledgment
We appreciate the collaboration of the Cuban Society of Imaging and the Hospitals “Luis Díaz Soto” (Naval), Institute of Tropical Medicine “Pedro Kouri” and “Salvador Allende” for providing us with the images that allowed us to develop this research.
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Garea-Llano, E., Castellanos-Loaces, H.A., Martinez-Montes, E., Gonzalez-Dalmau, E. (2021). A Machine Learning Based Approach for Estimation of the Lung Affectation Degree in CXR Images of COVID-19 Patients. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2021. Lecture Notes in Computer Science(), vol 13055. Springer, Cham. https://doi.org/10.1007/978-3-030-89691-1_2
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