Integrated global and local feature extraction and classification from computerized tomography (CT) images for lung cancer classification.

Despite being the second most often diagnosed form of cancer, lung cancers are rarely found in the general population. It is proposed in this study to employ a methodology of extracting both global and local features from CT scan images for the identification of lung cancer. Data gathering, globalised and localised training as well as testing the model are all part of this structure.