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

• URL: http://arxiv.org/abs/2009.11587v1
• Date: Thu, 24 Sep 2020 10:35:46 GMT
• Title: Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection
• Authors: Shah B. Shrey, Lukman Hakim, Muthusubash Kavitha, Hae Won Kim, Takio Kurita
• Abstract summary: 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.
• Score: 3.5068701342301547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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