  Lung cancer accounts for the highest number of cancer deaths globally. Early
diagnosis of lung nodules is very important to reduce the mortality rate of
patients by improving the diagnosis and treatment of lung cancer. This work
proposes an automated system to classify lung nodules as malignant and benign
in CT images. It presents extensive experimental results using a combination of
geometric and histogram lung nodule image features and different linear and
non-linear discriminant classifiers. The proposed approach is experimentally
validated on the LIDC-IDRI public lung cancer screening thoracic computed
tomography (CT) dataset containing nodule level diagnostic data. The obtained
results are very encouraging correctly classifying 82% of malignant and 93% of
benign nodules on unseen test data at best.
