Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial‐least‐square discriminant analysis and support vector machine

Li, Yuanpeng; Xie, Xiaojuan; Yang, Xinhao; Guo, Liangqia; Liu, Zhao; Zhao, Xiaoping; Luo, Ying; Wei, Jia; Huang, Furong; Zhu, Siqi; Chen, Zhenqiang; Chen, Xingdan; Zhong, Wenjun; Zhang, Weimin

doi:10.1002/jbio.201800324

Cited by 24 publications

(22 citation statements)

References 38 publications

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“…We noted that LOPOCV has not been consistently used in HSI research. Performance has often been evaluated by testing and training the model on the same hypercube [9,30,43]. This severely inflates performance and it does not reflect the real expected performance of the model on new patient data.…”

Section: Leave-one-patient-out Cross Validation (Lopocv)mentioning

confidence: 99%

Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging

et al. 2021

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There are approximately 1.8 million diagnoses of colorectal cancer, 1 million diagnoses of stomach cancer, and 0.6 million diagnoses of esophageal cancer each year globally. An automatic computer-assisted diagnostic (CAD) tool to rapidly detect colorectal and esophagogastric cancer tissue in optical images would be hugely valuable to a surgeon during an intervention. Based on a colon dataset with 12 patients and an esophagogastric dataset of 10 patients, several state-of-the-art machine learning methods have been trained to detect cancer tissue using hyperspectral imaging (HSI), including Support Vector Machines (SVM) with radial basis function kernels, Multi-Layer Perceptrons (MLP) and 3D Convolutional Neural Networks (3DCNN). A leave-one-patient-out cross-validation (LOPOCV) with and without combining these sets was performed. The ROC-AUC score of the 3DCNN was slightly higher than the MLP and SVM with a difference of 0.04 AUC. The best performance was achieved with the 3DCNN for colon cancer and esophagogastric cancer detection with a high ROC-AUC of 0.93. The 3DCNN also achieved the best DICE scores of 0.49 and 0.41 on the colon and esophagogastric datasets, respectively. These scores were significantly improved using a patient-specific decision threshold to 0.58 and 0.51, respectively. This indicates that, in practical use, an HSI-based CAD system using an interactive decision threshold is likely to be valuable. Experiments were also performed to measure the benefits of combining the colorectal and esophagogastric datasets (22 patients), and this yielded significantly better results with the MLP and SVM models.

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Section: Leave-one-patient-out Cross Validation (Lopocv)mentioning

confidence: 99%

Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging

et al. 2021

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“…The penalty parameter c and kernel function parameter g were used as two important parameters of RBF. These two parameters have important control impacts on model complexity, approximation error, and measurement accuracy of the model (Schlkopf and Smola, 2001;Aljarah et al, 2018;Li et al, 2019;Yalsavar et al, 2019). The penalty parameter c in the SVM model represents the degree of the penalty of an incorrect classification under linearly inseparable situations.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

Guo²,

et al. 2020

Front. Chem.

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Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400–2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300–1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400–1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.

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“…Youlin Tuo et al [28] constructed an SVM classifier to evaluate the possibility of breast cancer metastasis and obtained high classification accuracy in several independent data sets. Yuanpeng Li et al [29] used both SVM models and partial-least-square discriminant analysis to diagnose early gastric cancer. The experimental results showed the diagnostic model obtained from SVM was evidently better than the partial-least-square discriminant analysis.…”

Section: Related Workmentioning

confidence: 99%

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

2019

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As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

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Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial‐least‐square discriminant analysis and support vector machine

Cited by 24 publications

References 38 publications

Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging

Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

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