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 followed by a cancer predictor trained on the Kaggle DSB 2017 dataset and evaluated on the ISBI 2018 Lung Nodule Malignancy Prediction test set. 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. Our cancer predictor obtained a ROC AUC of 0.913 and was ranked 1st place at theISBI 2018 Lung Nodule Malignancy Prediction challenge.