In 2020, COVID-19 emerged, and several health units faced problems with the demand for chest Computed Tomography (CT), one of the ways of detecting lung lesions. One of the initial processes for treating CT is lung segmentation. Several computational methods have been proposed since they would accelerate the patient’s diagnosis process. This paper proposes a method for lung segmentation on CT in patients affected by COVID-19 using deep learning and adaptive convex hull. In this work, two databases were used: one with 38 and the other with 72 patients, without and with COVID-19, respectively. The promising results may assist in future work to detect pneumonia.
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