Lung cancer is the deadliest cancer worldwide. It has been shown that early detection using computer tomography (CT)
scans can reduce deaths caused by this disease. We present a general framework for the detection of lung cancer in chest CT images.
Our method consists of a nodule detector trained on the LIDC-IDRI dataset.
Our candidate extraction approach is
effective to produce precise candidates with a recall of 99.6%. In addition, false positive reduction stage manages to successfully
classify candidates and increases precision by a factor of 7.000.