The imaging process significantly impacts the understanding of the disease progression and helps to administer effective treatment much faster. However, most general-purpose algorithms detect any abnormality that CXRs contain. That's why it is challenging to develop a classifier, as there are lots of abnormal findings on CXRs. Meanwhile, we developed a DL model that precisely detects general abnormalities, unseen instances of tuberculosis, and COVID-19. The idea was to use computer vision, statistical and machine learning algorithms.
We provided a solution to detect abnormal chest X-rays. This algorithm automatically detects any pathological changes in the lungs (general abnormalities, unseen instances of tuberculosis, and COVID-19) and prioritizes them. Our solution comes with the code documentation, scientific report and analytics of the resultant data.
So this is how transforming CXRs data into a human-readable format would later boost medical professionals' estimation.