Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network | |
Liu, Song1,2; Wang, Quan1,3; Zhang, Geng1; Du, Jian1; Hu, Bingliang1,3; Zhang, Zhoufeng1,3 | |
作者部门 | 光谱成像技术研究室 |
2020-08-14 | |
发表期刊 | ANALYTICAL METHODS |
ISSN | 1759-9660;1759-9679 |
卷号 | 12期号:30页码:3844-3853 |
产权排序 | 1 |
摘要 | The gastric cancer grading of patients determines their clinical treatment plan. We use hyperspectral imaging (HSI) gastric cancer section data to automatically classify the three different cancer grades (low grade, intermediate grade, and high grade) and healthy tissue. This paper proposed the use of HSI data combined with a shallow residual network (SR-Net) as the classifier. We collected hyperspectral data from gastric sections of 30 participants, with the wavelength range of hyperspectral data being 374 nm to 990 nm. We compared the classification results between hyperspectral data and color images. The results show that using hyperspectral data and a SR-Net an average classification accuracy of 91.44% could be achieved, which is 13.87% higher than that of the color image. In addition, we applied a modified SR-Net incorporated direct down-sampling, asymmetric filters, and global average pooling to reduce the parameters and floating-point operations. Compared with the regular residual network with the same number of blocks, the floating-point operations of a SR-Net are one order of magnitude less. The experimental results show that hyperspectral data with a SR-Net can achieve cutting-edge performance with minimum computational cost and therefore have potential in the study of gastric cancer grading. |
DOI | 10.1039/d0ay01023e |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000556082200008 |
出版者 | ROYAL SOC CHEMISTRY |
EI入藏号 | 20204609491413 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/93633 |
专题 | 光谱成像技术研究室 |
通讯作者 | Hu, Bingliang; Zhang, Zhoufeng |
作者单位 | 1.Chinese Acad Sci, Key Lab Spectral Imaging Technol CAS, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Key Lab Biomed Spect Xian, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Liu, Song,Wang, Quan,Zhang, Geng,et al. Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network[J]. ANALYTICAL METHODS,2020,12(30):3844-3853. |
APA | Liu, Song,Wang, Quan,Zhang, Geng,Du, Jian,Hu, Bingliang,&Zhang, Zhoufeng.(2020).Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network.ANALYTICAL METHODS,12(30),3844-3853. |
MLA | Liu, Song,et al."Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network".ANALYTICAL METHODS 12.30(2020):3844-3853. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Using hyperspectral (1114KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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