Supervised Bayesian learning for breast cancer detection in terahertz imaging

Chavez, Tanny; Vohra, Nagma; Bailey, Keith; El-Shenawee, Magda; Wu, Jingxian

doi:10.1016/j.bspc.2021.102949

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…The model’s accuracy is heavily dependent on the quality of the training data. In this study, FFPE samples 22 were used as a reference point for the training purposes. However, the dehydration process can negatively impact the quality of the training data due to differences in shape between freshly excised samples and their FFPE counterparts.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Terra hertz technology has had a significant impact on the healthcare system, as extensively explored by numerous researchers and scientists. Advanced terahertz healthcare systems strive to boost medication adherence, diagnosis, and treatment efficacy through the utilization of multimedia technology and remote monitoring 22 – 26 . These systems encompass a variety of components such as medicine bags, home servers, hospital servers, prescription terminals, and pharmacy servers, thereby streamlining communication and data exchange 27 , 28 .…”

Section: Prior Literature Reviewmentioning

confidence: 99%

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Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging

Satpathy,

Khalaf,

Shukla

et al. 2024

Sci Rep

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The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Prior Literature Reviewmentioning

confidence: 99%

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging

Satpathy,

Khalaf,

Shukla

et al. 2024

Sci Rep

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show abstract

“…Ref. [40] proposed a supervised Bayesian learning algorithm for cancer detection using terahertz imaging. Specifically, they used a reliability-based training data selection method in order to only use for training those data that exceed a certain threshold.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty-driven ensembles of multi-scale deep architectures for image classification

et al. 2023

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“…Over the past two decades, terahertz time-domain spectroscopy (THz-TDS) has emerged as a promising technique for the non-invasive sensing of various biological tissues 28 , 29 . THz-TDS has been effective in the delineation of breast or skin cancer margins, 30 – 36 diagnosis of brain, 37 colon, 38 and gastric tumors, 39 screening diabetic foot syndrome, 40 , 41 and monitoring corneal hydration for diagnosing glaucoma 42 – 49 In skin assessment applications, THz-TDS has been used for quantifying hydration changes in the skin 50 …”

Section: Introductionmentioning

confidence: 99%

Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of terahertz time-domain waveforms

et al. 2022

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Significance: Severe burn injuries cause significant hypermetabolic alterations that are highly dynamic, hard to predict, and require acute and critical care. The clinical assessments of the severity of burn injuries are highly subjective and have consistently been reported to be inaccurate. Therefore, the utilization of other imaging modalities is crucial to reaching an objective and accurate burn assessment modality.Aim: We describe a non-invasive technique using terahertz time-domain spectroscopy (THz-TDS) and the wavelet packet Shannon entropy to automatically estimate the burn depth and predict the wound healing outcome of thermal burn injuries.Approach: We created 40 burn injuries of different severity grades in two porcine models using scald and contact methods of infliction. We used our THz portable handheld spectral reflection (PHASR) scanner to obtain the in vivo THz-TDS images. We used the energy to Shannon entropy ratio of the wavelet packet coefficients of the THz-TDS waveforms on day 0 to create supervised support vector machine (SVM) classification models. Histological assessments of the burn biopsies serve as the ground truth. Results:We achieved an accuracy rate of 94.7% in predicting the wound healing outcome, as determined by histological measurement of the re-epithelialization rate on day 28 post-burn induction, using the THz-TDS measurements obtained on day 0. Furthermore, we report the accuracy rates of 89%, 87.1%, and 87.6% in automatic diagnosis of the superficial partialthickness, deep partial-thickness, and full-thickness burns, respectively, using a multiclass SVM model. Conclusions:The THz PHASR scanner promises a robust, high-speed, and accurate diagnostic modality to improve the clinical triage of burns and their management.

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Supervised Bayesian learning for breast cancer detection in terahertz imaging

Cited by 16 publications

References 23 publications

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging

Uncertainty-driven ensembles of multi-scale deep architectures for image classification

Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of terahertz time-domain waveforms

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