Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

Ling Ma, Guolan Lu, Dongsheng Wang, Xu Wang, Zhuo Georgia Chen, Susan Muller, Amy Chen, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510607194
DOIs
StatePublished - 2017
Externally publishedYes
EventMedical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging - Orlando, United States
Duration: Feb 12 2017Feb 14 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10137
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityOrlando
Period2/12/172/14/17

Keywords

  • Convolutional neural networks (CNN)
  • Head and neck cancer
  • Hyperspectral imaging
  • Machine learning
  • Noninvasive cancer detection
  • Spectral-spatial classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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