Differential optical absorption spectroscopy-based refractive index sensor for cancer cell detection

Parvin, Tarunnum; Ahmed, Kawsar; Alatwi, Aadel M.; Rashed, Ahmed Nabih Zaki

doi:10.1007/s10043-021-00644-w

Cited by 123 publications

(22 citation statements)

References 19 publications

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“…Based on a literature study, the assignment of individual samples with known refractive index values to two classes (healthy or cancer) was defined 27 , 28 . The predictive capabilities of selected supervised machine learning algorithms were built and analyzed to select the optimal classification model.…”

Section: Methodsmentioning

confidence: 99%

Predictions of cervical cancer identification by photonic method combined with machine learning

Kruczkowski

Drabik-Kruczkowska

Marciniak

et al. 2022

Sci Rep

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Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing an optical sensor and a prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing four algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes, Convolutional Neural Networks) and assessment of their performance for classification of tissue as healthy or sick. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.

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Section: Methodsmentioning

confidence: 99%

Predictions of cervical cancer identification by photonic method combined with machine learning

Kruczkowski

Drabik-Kruczkowska

Marciniak

et al. 2022

Sci Rep

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“…1. to two classes (healthy or cancer) was defined 19,20 . The predictive capabilities of selected supervised machine learning algorithms were built and analyzed to select the optimal classification model.…”

Section: Methodsmentioning

confidence: 99%

Machine learning for predictions of cervical cancer identification – preliminary investigation based on refractive index

Kruczkowski

Drabik-Kruczkowska

Marciniak

et al. 2021

Preprint

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Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors – to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing optical sensor and prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing three algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes) and assessment of their performance for classification of tissue as healthy or sick. All of them provided high values (>89%) of the measures describing them. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.

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“…We can obtain the coefficient 1 2 3 4 1 2 3 4 , , , , , , , c c c c a a a a by substituting equations ( 4)-( 7) into ( 9)- (11). This way, we can obtain expressions for the impact force and various dynamic responses.…”

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confidence: 99%

Mathematical Differential Equation in Calculating the Strength of Building Beam Structure Impact Resistance

Chen

Mohamed

2022

Applied Mathematics and Nonlinear Sciences

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This paper establishes a numerical simulation model of the impact resistance of building beam structures by using differential equations. The model fully considers the bolt preload, contact, large nonlinear deformation, and strain rate effect of the building beam structure. The paper establishes and deduces the calculation formulas of the impact force and the lateral displacement of the beam. The study found that under the impact load of the same strength, the failure form of the building beam structure is serious at the assembly point. The local macro/microsimulation results agree with the experimental results. The paper's results verify the rationality and effectiveness of the numerical method in this paper.

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Differential optical absorption spectroscopy-based refractive index sensor for cancer cell detection

Cited by 123 publications

References 19 publications

Predictions of cervical cancer identification by photonic method combined with machine learning

Predictions of cervical cancer identification by photonic method combined with machine learning

Machine learning for predictions of cervical cancer identification – preliminary investigation based on refractive index

Mathematical Differential Equation in Calculating the Strength of Building Beam Structure Impact Resistance

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