Detection of breast cancer of various clinical stages based on serum FT-IR spectroscopy combined with multiple algorithms

Yang, Bo; Chen, Chen; Chen, Cheng; Cheng, Hong; Yan, Ziwei; Chen, Fangfang; Zhu, Zhaojie; Huiting, Zhang; Yue, Feilong; Lv, Xiaoyi

doi:10.1016/j.pdpdt.2021.102199

Cited by 19 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tis result also coincides with the marked diferences in protein content between cancer patients and the normal population in clinical practice. At the same time, this is consistent with similar studies in the same category [29,30,32,33]. Tis further demonstrates the important clinical application potential of spectroscopy.…”

Section: Diference Spectral Analysissupporting

confidence: 89%

“…Tis result may be related to the absorption of the amide structure of the protein with the α-helical structure. It refects the diference in protein content and its secondary structure, which is consistent with the fndings of Zelig and Yang [29,30]. Te spectral diference between lung cancer and normal people not only refects the amide I region (1398.62-1543.74 cm −1 ) [31][32][33] corresponding to protein absorption but also refects the phosphate group region of RNA and DNA (1172.99-1319.55 cm −1 ).…”

Section: Diference Spectral Analysissupporting

confidence: 83%

“…Te spectral diference between lung cancer and normal people not only refects the amide I region (1398.62-1543.74 cm −1 ) [31][32][33] corresponding to protein absorption but also refects the phosphate group region of RNA and DNA (1172.99-1319.55 cm −1 ). Spectral diferences between breast and lung cancers were also signifcant in the range 1318.59-1515.77 cm −1 , a region containing the absorption of key structures of lipids and phospholipids [30,34]. In general, the spectra of the diferent populations are signifcantly diferent or can be efectively distinguished.…”

Section: Diference Spectral Analysismentioning

confidence: 95%

See 2 more Smart Citations

Application of Serum Mid-Infrared Spectroscopy Combined with Machine Learning in Rapid Screening of Breast Cancer and Lung Cancer

Zhu,

Shen,

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Cancer is an increasing burden on global health. Breast and lung cancers are the two tumors with the highest incidence rates. The study shows that early detection and early diagnosis are important prognostic factors for breast and lung cancers. Due to the great advantages of artificial intelligence in feature extraction, the combination of infrared analysis technology may have great potential in clinical applications. This study explores the potential application of mid-infrared spectroscopy combined with machine learning for the differentiation of breast and lung cancers. The experiment collects blood samples from clinical sources, separates serum, trains classification models, and finally predicts unknown sample categories. We use k-fold cross-validation to determine the training set of 301 cases and the test set of 50 cases. Through differential spectrum analysis, we found that the intervals of 1318.59–1401.03 cm−1, 1492.15–1583.27 cm−1, and 1597.25–1721.64 cm−1 have significant differences, which may reflect the absorption of key chemical bonds in protein molecules. We use a total of 24 models such as decision trees, discriminant analysis, support vector machines, and K-nearest neighbor to train, identify, and distinguish spectra. The results show that under the same conditions, the prediction model trained based on fine KNN has the best performance and can perform 100% prediction on the test set samples. This also shows that our model has important potential for auxiliary diagnosis of serum breast cancer and lung cancer. This method may help to further achieve comprehensive screening of associated cancers in underserved areas, thereby reducing the cancer burden through early detection of cancer and appropriate treatment and care of cancer patients.

show abstract

Section: Diference Spectral Analysissupporting

confidence: 89%

Section: Diference Spectral Analysissupporting

confidence: 83%

Section: Diference Spectral Analysismentioning

confidence: 95%

See 1 more Smart Citation

Application of Serum Mid-Infrared Spectroscopy Combined with Machine Learning in Rapid Screening of Breast Cancer and Lung Cancer

Zhu,

Shen,

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…It also finds late‐stage breast cancer in a considerable proportion of women, reducing survival proportionally by 2019 WHO. Breast cancer incidence is thoroughly investigated in developed and developing nations (Sivasangari et al, 2022; Tang et al, 2021; Yang et al, 2021). Worldwide, breast cancer survival rates range from 80% to around 60% in middle‐income nations to fewer than 40% in developing and less‐income nations.…”

Section: Introductionmentioning

confidence: 99%

Infrared thermal images using PCSAN‐Net‐DBOA: An approach of breast cancer classification

Vijayarajan,

Manoj Kumar,

Sudha

et al. 2024

Microscopy Res & Technique

View full text Add to dashboard Cite

This manuscript proposes thermal images using PCSAN‐Net‐DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR‐IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched‐filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one‐dimensional quantum integer wavelet S‐transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD‐PCSANN‐DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD‐PCSANN‐DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD‐CNN), breast cancer classification from thermal images utilizing Grunwald‐Letnikov assisted dragonfly algorithm‐based deep feature selection (BCD‐VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD‐SqueezeNet), respectively.Research Highlights The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD‐PCSANN‐DBO method is implemented using Python.

show abstract

“…The application of computer vision in medical imaging is mainly divided into two categories, namely computer-aided diagnosis (CADx) and computer-aided detection (CADe). The CADx can classify, recognize, and predict diseases [ 5 ]. However, treating medical imaging as a classification problem, the task setting is too ambitious and extensive, and it cannot be regarded as solving medical problems.…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection

Dong

2022

Entropy

View full text Add to dashboard Cite

Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability. The spiking neural network is considered as the third-generation artificial neural network, which is event-driven and has low power consumption. It modulates the process of nerve cells from receiving a stimulus to firing spikes. However, it is difficult to train spiking neural network directly due to the non-differentiable spiking neurons. In particular, it is impossible to train a spiking neural network using the back-propagation algorithm directly. Therefore, the application scenarios of spiking neural network are not as extensive as deep neural network, and a spiking neural network is mostly used in simple image classification tasks. This paper proposed a spiking neural network method for the field of object detection based on medical images using the method of converting a deep neural network to spiking neural network. The detection framework relies on the YOLO structure and uses the feature pyramid structure to obtain the multi-scale features of the image. By fusing the high resolution of low-level features and the strong semantic information of high-level features, the detection precision of the network is improved. The proposed method is applied to detect the location and classification of breast lesions with ultrasound and X-ray datasets, and the results are 90.67% and 92.81%, respectively.

show abstract

Detection of breast cancer of various clinical stages based on serum FT-IR spectroscopy combined with multiple algorithms

Cited by 19 publications

References 38 publications

Application of Serum Mid-Infrared Spectroscopy Combined with Machine Learning in Rapid Screening of Breast Cancer and Lung Cancer

Application of Serum Mid-Infrared Spectroscopy Combined with Machine Learning in Rapid Screening of Breast Cancer and Lung Cancer

Infrared thermal images using PCSAN‐Net‐DBOA: An approach of breast cancer classification

Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection

Contact Info

Product

Resources

About