13 March 2017 Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model
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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.
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Ling Ma, Guolan Lu, Dongsheng Wang, Xu Wang, Zhuo Georgia Chen, Susan Muller, Amy Chen, Baowei Fei, "Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372G (13 March 2017); doi: 10.1117/12.2255562; https://doi.org/10.1117/12.2255562
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