Computer aided lung cancer diagnosis with deep learning algorithms

Wenqing Sun; Bin Zheng; Wei Qian

doi:10.1117/12.2216307

24 March 2016 Computer aided lung cancer diagnosis with deep learning algorithms

Wenqing Sun, Bin Zheng, Wei Qian

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97850Z (2016) https://doi.org/10.1117/12.2216307
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

Citation Download Citation

Wenqing Sun, Bin Zheng, and Wei Qian "Computer aided lung cancer diagnosis with deep learning algorithms", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850Z (24 March 2016); https://doi.org/10.1117/12.2216307

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