CAD4TB is a computer-aided detection system whose main goal is to detect tuberculosis-related abnormalities in posterior anterior chest X-rays. CAD4TB takes a single chest X-ray as its input, in the form of a DICOM image, and produces several outputs: a quality assessment of the input image, a heat map highlighting possible abnormal areas, and a score between 0 and 100 indicating the likelihood of the X-ray being abnormal and the subject on the X-ray being affected by tuberculosis.
How was CAD4TB trained?
CAD4TB has been developed following the principles of supervised machine learning: in the process of computing its score, it compares regions in the input image with regions extracted from normal and abnormal images previously processed by the system, which constitute the so-called training set. One of the conditions for proper supervised learning is that this training set should be representative of the test data; otherwise, results may not be reliable. To fulfill this condition, and thus make CAD4TB applicable to diverse scenarios, the system has been trained with data from several populations and several X-ray devices.
Partnering with Delft Imaging Systems
Delft Imaging Systems develops affordable diagnostic imaging innovations, with a focus on the screening for tuberculosis. Delft is the exclusive distributor of the CAD4TB software and has spearheaded the succesful activation of CAD4TB in over thirty countries globally.