@inproceedings{8e08b372ede34278876b6164fd348190,
title = "Transfer Learning by Cascaded Network to Identify and Classify Lung Nodules for Cancer Detection",
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.",
keywords = "Cascade network, Classification, CT images, Deep learning, Image segmentation, Lung nodule",
author = "Shrey, \{Shah B.\} and Lukman Hakim and Muthusubash Kavitha and Kim, \{Hae Won\} and Takio Kurita",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; International Workshop on Frontiers of Computer Vision, IW-FCV 2020 ; Conference date: 20-02-2020 Through 22-02-2020",
year = "2020",
doi = "10.1007/978-981-15-4818-5\_20",
language = "English (US)",
isbn = "9789811548178",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "262--273",
editor = "Wataru Ohyama and Jung, \{Soon Ki\}",
booktitle = "Frontiers of Computer Vision - 26th International Workshop, IW-FCV 2020, Revised Selected Papers",
}