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Journal of Biomedical Optics • Open Access

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer
Author(s): Sheng Weng; Xiaoyun Xu; Jiasong Li; Stephen T. C. Wong

Paper Abstract

Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.

Paper Details

Date Published: 30 October 2017
PDF: 10 pages
J. Biomed. Opt. 22(10) 106017 doi: 10.1117/1.JBO.22.10.106017
Published in: Journal of Biomedical Optics Volume 22, Issue 10
Show Author Affiliations
Sheng Weng, Rice Univ. (United States)
The Methodist Hospital Research Institute (United States)
Xiaoyun Xu, The Methodist Hospital Research Institute (United States)
Jiasong Li, The Methodist Hospital Research Institute (United States)
Stephen T. C. Wong, The Methodist Hospital Research Institute (United States)
Rice Univ. (United States)

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