End-to-End Deep Learning Strategies for Computer-Aided Lung Cancer Detection Systems

Volume 4, Issue 5, October 2019     |     PP. 140-155      |     PDF (459 K)    |     Pub. Date: November 7, 2019
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Bohdan Chapaliuk, Institute for Aplied System Analysis, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
Yuriy Zaychenko, Institute for Aplied System Analysis, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

Lung cancer is one of the most aggressive types of cancer, and the possibility to detect it at an early stage can save a lot of patient lives worldwide. Building an automated lung cancer detection system can help to speed up the process of cancer detection and save human lives. This article considers three different approaches used to design and build lung cancer detection systems. Mainly, these approaches use either the 2D convolutional neural networks (CNN) with a multi-instance learning task, 2D CNN with recurrent neural networks, or detection system pipelines with 3D CNNs, which can consist of single or multiple stages. The article presents the results of the experiments for each of the approaches. Finally, the obtained results and results from other recent papers are combined to compare all existing lung cancer detection system architectures and evaluate how different design decisions impact overall system accuracy.

deep learning, convolutional neural network, lung cancer detection system

Cite this paper
Bohdan Chapaliuk, Yuriy Zaychenko, End-to-End Deep Learning Strategies for Computer-Aided Lung Cancer Detection Systems , SCIREA Journal of Mathematics. Volume 4, Issue 5, October 2019 | PP. 140-155.


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