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Proceedings Paper

Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model
Author(s): Ling Ma; Guolan Lu; Dongsheng Wang; Xu Wang; Zhuo Georgia Chen; Susan Muller; Amy Chen; Baowei Fei
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Paper Abstract

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

Paper Details

Date Published: 13 March 2017
PDF: 8 pages
Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372G (13 March 2017); doi: 10.1117/12.2255562
Show Author Affiliations
Ling Ma, Emory Univ. (United States)
Beijing Institute of Technology (China)
Guolan Lu, Georgia Institute of Technology (United States)
Emory Univ. (United States)
Dongsheng Wang, Emory Univ. (United States)
Xu Wang, Emory Univ. (United States)
Zhuo Georgia Chen, Emory Univ. (United States)
Susan Muller, Emory Univ. School of Medicine (United States)
Amy Chen, Emory Univ. School of Medicine (United States)
Baowei Fei, Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10137:
Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor Gimi, Editor(s)

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