Classification of lung nodules in CT images using convolutional neural networks

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2018
Polat, Görkem
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Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. The number of slices in a CT scan can be up to 600. Therefore, computeraided-detection (CAD) systems are very important for faster and more accurate assessment of the data. In this thesis, we proposed a framework that analyzes CT lung screenings using convolutional neural networks (CNNs) to reduce false positives. Our framework shows that even non-complex architectures are very powerful to classify 3D nodule data when compared to traditional methods. We trained our model with different volume sizes and showed that volume size plays a critical role in the performance of the system. We also used different fusions in order to show their power and effect on the overall accuracy. 3D CNNs were preferred over 2D CNNs because data was in 3D and 2D convolutional operations may result in information loss. The proposed framework has been tested on the dataset provided by the LUNA16 Challenge and got a sensitivity of 0.831 at 1 false positive per scan.
Lungs, Lungs, Tomography., Neural networks (Computer science) .
http://etd.lib.metu.edu.tr/upload/12621875/index.pdf
https://hdl.handle.net/11511/27099
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
G. Polat, “Classification of lung nodules in CT images using convolutional neural networks,” M.S. - Master of Science, Middle East Technical University, 2018.
METU IIS - Integrated Information Service open@metu.edu.tr