Micro-tomographic and infrared spectral data mining for breast cancer diagnosis

Guo, Shanshan; Xiu, Junshan; Kong, Lingnan; Kong, Xin; Wang, Hanqiu; Lü, Zhiwei; Xu, Fengli; Li, Jing; Ji, Te; Wang, Fuli; Liu, Huiqiang

doi:10.1016/j.optlaseng.2022.107305

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Infrared thermal imaging uses signal processing, photoelectric conversion and other technical measures to convert infrared radiation in the target area into visual images 1 . It has the advantages of round-the-clock operation, good anti-jamming and not easy to be detected 2 , therefore, it is widely used in medical images 3 , industrial equipment 4 , security equipment 5 and military operations 6 .…”

Section: Introductionmentioning

confidence: 99%

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Zhang,

Zhou,

Chen

2024

Sci Rep

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Infrared thermal imaging is a passive non-contact detection and identification technology, which is not subject to electromagnetic infection and good concealment, is widely used in military and commercial fields. However, due to the limitations of the existing infrared imaging system mechanisms, the spatial resolution of the acquired infrared images is low and the edge details are blurred, which in turn leads to poor performance in downstream missions based on infrared images. In this paper, in order to better solve the above problems, we propose a new super-resolution reconstruction framework for infrared images, called DBFE, which extracts and retains abundant structure and textual information for robust infrared image high-resolution reconstruction with a novel structure-textual encoder module. Extensive experiment demonstrates that our proposed method achieves significantly superior contraband high-resolution reconstruction results on the multiple dataset compared to progressive methods for high resolution infrared image reconstruction, effectively proving the practicability of the method proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Zhang,

Zhou,

Chen

2024

Sci Rep

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“…It is caused by irregular and accelerated breast growth cells. These tumors eventually evolve into malignant nodular tumors (He et al, 2021; Guo et al, 2023; Ghaheri et al, 2023). Every year, at least 10,000 new cases of breast cancer are reported in India by the World Health Organization (WHO).…”

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

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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.

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“…Synchrotron radiation (SR) light source has the advantage of brightness at least two or three orders of magnitude higher than traditional light source (globar), with an improved spectral signal-to-noise ratio (SNR) of samples [ 26 ]. Thus, SR-based ATR-FTIR microscopy facilitates the high-precision in-situ measurement of fresh samples, especially achieving specific spectrum collections of microregion-of-interest [ 27 ]. With the combination of data mining (DM) methods, such as principal component analysis (PCA), Hierarchical cluster analysis (HCA), Partial least squares discriminant analysis (PLS-DA), and machine learning (ML) classifiers, the FTIR-based intelligent diagnosis has been widely employed into Corona Virus Disease 2019 (COVID-19) screening [ 28 ], health monitoring [ 29 ], disease detection [ 30 ], and food security.…”

Section: Introductionmentioning

confidence: 99%

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

et al. 2022

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Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

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Micro-tomographic and infrared spectral data mining for breast cancer diagnosis

Cited by 7 publications

References 43 publications

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

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

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

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