Analysis and classification of oral tongue squamous cell carcinoma based on Raman spectroscopy and convolutional neural networks |
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Authors: | Jiabin Xia Mingxin Yu Tao Zhang Zhihui Zhu Xiaoping Lou |
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Affiliation: | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei, People’s Republic of China;2. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, People’s Republic of China;3. Department of Stomatology, Peking Union Medical College Hospital, Beijing, People’s Republic of China |
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Abstract: | ABSTRACTTo detect oral tongue squamous cell carcinoma (OTSCC) using fibre optic Raman spectroscopy, we present a classification model based on convolutional neural networks (CNN) and support vector machines (SVM). 24 samples Raman spectra of OTSCC and para-carcinoma tissues from 12 patients were collected and analysed. In our proposed model, CNN is used as a feature extractor for forming a representative vector. Then the derived features are fed into an SVM classifier, which is used for OTSCC classification. Experimental results demonstrated that the area under the receiver operating characteristic curve was 99.96% and the classification error was zero (sensitivity: 99.54%, specificity: 99.54%). To show the superiority of this model, comparison results with the state-of-the-art methods showed it can obtain a competitive accuracy. These findings may pay a way to apply the proposed model in the fibre optic Raman instruments for intra-operative evaluation of OTSCC resection margins. |
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Keywords: | Fibre optic Raman oral tongue squamous cell carcinoma convolutional neural networks (ConvNets) deep learning spectroscopy |
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