  Lung cancer is one of the most deadly diseases in the world. Detecting such
tumors at an early stage can be a tedious task. Existing deep learning
architecture for lung nodule identification used complex architecture with
large number of parameters. This study developed a cascaded architecture which
can accurately segment and classify the benign or malignant lung nodules on
computed tomography (CT) images. The main contribution of this study is to
introduce a segmentation network where the first stage trained on a public data
set can help to recognize the images which included a nodule from any data set
by means of transfer learning. And the segmentation of a nodule improves the
second stage to classify the nodules into benign and malignant. The proposed
architecture outperformed the conventional methods with an area under curve
value of 95.67\%. The experimental results showed that the classification
accuracy of 97.96\% of our proposed architecture outperformed other simple and
complex architectures in classifying lung nodules for lung cancer detection.
