Non-Ionic Deep Learning-Driven IR-UWB Multiantenna Scheme for Breast Tumor Localization

Phasukkit, Pattarapong

doi:10.1109/access.2021.3140083

Cited by 6 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. + Z H(2,4) (5) In the backpropagation, the cross-entropy between the normalized training output dataset (Y train ; Y n ) and the predicted normalized output ( Ŷn ) is first calculated using Equation ( 6) [26]:…”

Section: The Time-series Deep-learning Classification Modelmentioning

confidence: 99%

“…The performance of the time-series deep-learning classification model was assessed by the F1 score and the total accuracy (the average of the F1 scores) [26]. The F1 score is a value that indicates the classification performance of an algorithmic model based on Precision (Equation ( 9)) and Recall (Equation ( 10)).…”

Section: The Time-series Deep-learning Classification Modelmentioning

confidence: 99%

See 1 more Smart Citation

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

Puangragsa

Setakornnukul

Dankulchai

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, ‘pseudopatients’ refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) of 1.2–1.6%, 0.65–0.8%, and 0.97–0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.

show abstract

Section: The Time-series Deep-learning Classification Modelmentioning

confidence: 99%

Section: The Time-series Deep-learning Classification Modelmentioning

confidence: 99%

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

Puangragsa

Setakornnukul

Dankulchai

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, a signifi-cant number of studies have also explored the use of machine learning and deep learning for microwave imaging systems. Some of these works deal with image reconstruction [20]- [22], classification [23]- [25], feature recognition [26]- [28] and localization [29]- [31]. Another area that recently received significant traction is the solution of the non-linear inverse problem in radar imaging through deep learning approaches [32]- [34].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Sensing Matrix Prediction in Computational Microwave Imaging With Coded-Apertures

Zhang,

Sharma,

García-Fernández

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This work aims to simplify the characterization process of coded-apertures for computational imaging (CI) at microwave frequencies. A major benefit of the presented technique is the minimization of the processing time needed to calculate the system sensing matrix for microwave CI-based compressive sensing applications. To achieve this, a deep learning-based approach is proposed which is capable of generating the sensing matrix using features learned directly from the coded-aperture distribution. To avoid the vanishing gradient problem, the proposed deep learning network contains skip connections. On 1,000 testing samples, the average normalized mean-squared-error (NMSE) calculated between the sensing matrix generated by the conventional method and that predicted by the proposed network is 0.0036. Moreover, the average meansquared-error (MSE) calculated between the images reconstructed using the conventional and predicted sensing matrix is 0.00297. In addition to providing high-fidelity estimations with minimized error, we demonstrate that using the trained network, the prediction of the sensing matrix can be achieved in 0.212 s, corresponding to a reduction of 65% in the computation time needed to calculate the sensing matrix. This has significant outcomes in achieving real-time operation of CI-based microwave imaging systems.

show abstract

“…Breast cancer alone is thought to have killed more than 508,000 women worldwide in 2011. Although it is considered that this mostly occurs in developed nations, the fact that 58% of breast cancer fatalities and 50% of cases occur in both wealthy and developing nations raises serious concerns (Nogales et al, 2022; Phasukkit, 2022; Gonzalez‐Rodriguez et al, 2024). Early detection of symptoms reduces treatment expenses greatly.…”

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

Non-Ionic Deep Learning-Driven IR-UWB Multiantenna Scheme for Breast Tumor Localization

Cited by 6 publications

References 28 publications

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

Deep Learning for Sensing Matrix Prediction in Computational Microwave Imaging With Coded-Apertures

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

Contact Info

Product

Resources

About