Visualization of pathological processes plays a crucial role in establishing most clinical diagnoses. However, sometimes the results could be controversial. Various factors influence it: the quality of the equipment, lack of medical skills and knowledge, limited time, etc. One of our clients decided to develop an algorithm for a medical image segmentation solution to distinguish between normal and abnormal chest X-rays (CXR) images. Read on to find out details.
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.