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Lung cancer is one of the most deadly diseases in the world. Detecting suchtumors at an early stage can be a tedious task. Existing deep learningarchitecture for lung nodule identification used complex architecture withlarge number of parameters. This study developed a cascaded architecture whichcan accurately segment and classify the benign or malignant lung nodules oncomputed tomography (CT) images. The main contribution of this study is tointroduce a segmentation network where the first stage trained on a public dataset can help to recognize the images which included a nodule from any data setby means of transfer learning. And the segmentation of a nodule improves thesecond stage to classify the nodules into benign and malignant. The proposedarchitecture outperformed the conventional methods with an area under curvevalue of 95.67\%. The experimental results showed that the classificationaccuracy of 97.96\% of our proposed architecture outperformed other simple andcomplex architectures in classifying lung nodules for lung cancer detection.

Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection