Diverse Region-Based CNN for Tongue Squamous Cell Carcinoma Classification With Raman Spectroscopy

Abstract

Border discrimination is very important in the treatment of tongue squamous cell carcinoma (TSCC). This study proposes an ensemble convolutional neural network (CNN) framework based on fiber optic Raman spectroscopy and deep learning techniques to distinguish between TSCC and non-tumor tissue frameworks. First, the data used in the experiments was collected by a fiber optic Raman system. A total of 44 tissues of 22 patients were collected for Raman spectroscopy, with TSCC and adjacent normal tissues each accounting for half. The spectral data range used in the model from a full spectrum of 600-4000 cm-1. Then, the ensemble CNN model was used in the experiment. By using two convolution kernels, the model is able to extract nonlinear feature representations from different spectral regions. It has two advantages, on the one hand, it reduces the generation of noise, on the other hand, it obtains a stronger distinguishing ability. Finally, a feature vector is formed by the fusion layer, and is sent to the fully connected layer for the TSCC classification task. The results showed that the sensitivity and specificity of the model were 99.2% and 99.2%, respectively. In addition, comparison with existing methods shows that our method achieves the highest accuracy of TSCC classification. By comparing the different channels, the results show that the spectral range of 1380-2250cm-1 data has the greatest impact on the results. Therefore, Raman spectroscopy combined with the ensemble CNN model has great potential and can provide a useful technique for intraoperative evaluation of the margins of oral tongue squamous cell carcinoma.

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

“…A total of 34 studies met the eligibility criteria and were included in this review [ 2 , 15 – 45 , 48 ]. The details of the study selection process have been described using the PRISMA flowchart ( Figure 1 ) [ 46 ].…”

“…The majority of these studies used a convolutional neural network (CNN) [ 2 , 15 – 22 , 24 – 26 , 28 , 31 – 36 , 38 – 41 , 43 – 45 , 48 , 49 ]. Several data types such as gene expression data [ 15 , 45 ], spectra data [ 20 , 21 , 29 , 34 , 37 , 44 , 48 ], and other image data types—anatomical [ 16 ], intraoral [ 17 ], histology [ 18 , 27 ], auto-fluorescence [ 19 , 22 ], cytology-image [ 23 ], neoplastic [ 40 ], clinical [ 28 , 36 , 38 ], oral lesions [ 42 ], computed tomography images [ 24 – 26 , 33 , 35 , 41 , 49 ], clinicopathologic [ 2 ], saliva metabolites [ 31 ], histopathological [ 30 , 32 , 43 ], and pathological [ 39 ] images have been used in the included studies.…”

“…Similarly, a total of 11 studies reported the combination of the trio of sensitivity, specificity, and accuracy as the performance metrics for the deep machine learning method [ 15 , 19 – 21 , 24 , 25 , 30 , 35 , 37 , 38 , 42 ]. Both specificity and sensitivity were used to depict the performance of the model [ 17 , 20 , 22 , 27 , 48 ]. Additionally, specificity and accuracy were also used to demonstrate the performance of the deep learning model for prognostication in OSCC [ 23 ].…”

“…Most of these studies used either spectra data or computed tomography images [ 20 , 21 , 24 – 26 , 29 , 33 – 35 , 37 , 41 , 44 , 48 , 49 ]. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively.…”

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

“…For example, for precise diagnosis purposes, deep learning models have been used in the detection of oral cancer [24,25,[64][65][66][67][68][69][70][71][72][73][74][75]. Additionally, these models have assisted in the prediction of lymph node metastasis [27][28][29]76].…”