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.
CITATION STYLE
Shrey, S. B., Hakim, L., Kavitha, M., Kim, H. W., & Kurita, T. (2020). Transfer Learning by Cascaded Network to Identify and Classify Lung Nodules for Cancer Detection. In Communications in Computer and Information Science (Vol. 1212 CCIS, pp. 262–273). Springer. https://doi.org/10.1007/978-981-15-4818-5_20
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