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arXiv · 2009.11587

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

Shah B. Shrey·Lukman Hakim·Muthusubash Kavitha·Hae Won Kim·Takio Kurita
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Abstract

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.

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Shah B. Shrey, Lukman Hakim, Muthusubash Kavitha, Hae Won Kim, Takio Kurita. 2020-09-24. Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection. https://arxiv.org/abs/2009.11587

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