Machine learning techniques for classification of breast tissue

Helwan, Abdulkader; Idoko, John Bush; Abiyev, Rahib H.

doi:10.1016/j.procs.2017.11.256

Cited by 44 publications

(17 citation statements)

References 27 publications

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“…5 b indicates that accuracies achieved were >86% at approximately half of measured frequency points (21 points among all 43 frequency points). However, our obtained accuracies are lower than that of Yilmaz et al [12 ] and Helwan et al [13 ]. The accuracies they obtained are as high as 99.2 and 97.8%, respectively.…”

Section: Discussioncontrasting

confidence: 94%

“…Yilmaz et al [12 ] reported a machine‐learning‐aided method to discriminate between malignant and healthy rat liver tissues based on discrepancies in dielectric properties at frequencies ranging from 500 MHz to 6 GHz. Helwan et al [13 ] measured the electrical impedance of breast tissue and classified the breast tissue automatically using the backpropagation learning algorithm and the radial basis function network. The machine‐learning‐aided methods can not only increase the accuracy of a single measurement [12 ] but also provide the basis for accurate determination of surgical margins [14 ].…”

Section: Introductionmentioning

confidence: 99%

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Discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties using machine learning methods

Sun

Zhang

Duan

et al. 2020

IET Science, Measurement &Amp; Technology

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Numerous researchers approved discrepancies in dielectric properties between malignant and normal tissues. Such discrepancies serve as a foundation for the development of computer‐aided diagnostic technologies. In this study, machine learning methods were proposed for discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties. To do so, first, two independent‐sample t ‐tests and receiver operating characteristic curve analysis were utilised to examine discrimination power with respect to three types of features, namely, permittivity, conductivity and Cole–Cole fitting parameters. K ‐nearest neighbour and support vector machine classifiers were used to assess the possibility of combining these features for better classification accuracy. Obtained k ‐fold cross‐validation accuracy reached 88.2%. The obtained accuracy indicated the potential capability of discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties.

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Section: Discussioncontrasting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties using machine learning methods

Sun

Zhang

Duan

et al. 2020

IET Science, Measurement &Amp; Technology

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“…With the popularity of lung cancer screening, the number and proportion of people diagnosed with early-stage disease is increasing. Surgery is considered the most effective treatment follow by classification by SVM (22)(23)(24)(25)(26), linear discriminant analysis (27), kNN (28), BP neural network (29), and RBF neural network (29). When processing data, the model parameters obtained by data fitting have certain volatility, which affects the identification results.…”

Section: Discussionmentioning

confidence: 99%

“…Among the few known papers, most of them model the fit for dielectric parameter data of tissue samples in a wide frequency band. The fitted model parameters were taken as feature vectors, follow by classification by SVM ( 22 – 26 ), linear discriminant analysis ( 27 ), kNN ( 28 ), BP neural network ( 29 ), and RBF neural network ( 29 ). When processing data, the model parameters obtained by data fitting have certain volatility, which affects the identification results.…”

Section: Discussionmentioning

confidence: 99%

Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks

Wang

et al. 2021

Front. Oncol.

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ObjectiveDielectric properties can be used in normal and malignant tissue identification, which requires an effective classifier because of the high throughput nature of the data. With easy training and fast convergence, probabilistic neural networks (PNNs) are widely applied in pattern classification problems. This study aims to propose a classifier to identify metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.MethodsThe dielectric properties (permittivity and conductivity) of lymph nodes were measured using an open-ended coaxial probe. The Synthetic Minority Oversampling Technique method was adopted to modify the dataset. Feature parameters were scored to select the appropriate feature vector using a Statistical Dependency algorithm. The dataset was classified using adaptive PNNs with an optimized smooth factor using the simulated annealing PNN (SA-PNN). The results were compared with traditional Probabilistic, Support Vector Machines, k-Nearest Neighbor and the Classify functions in MATLAB.ResultsThe conductivity frequencies of 3959, 3958, 3960, 3978, 3510, 3889, 3888, and 3976 MHz were selected as the feature vectors for 219 lymph nodes (178 non-metastatic and 41 metastatic). Compared with the other methods, SA-PNN achieved the highest classification accuracy (92.92%) and the corresponding specificity and sensitivity were 94.72% and 91.11%, respectively.ConclusionsCompared with the other methods, the SA-PNN proposed in the present study achieved a higher classification accuracy, which provides a new scheme for classification of metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.

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“…However, with sufficient data size, ANNs can preserve predictive information and be robust against outliers and overfitting. These attributes have been utilized for EIS based classification of breast tissue [ 40 , 42 ]. Both works use the same publicly available dataset of EIS measurements from freshly excised breast tissue [ 103 ], made available on the University of California, Irvine (UCI) Machine Learning Repository [ 104 ].…”

Section: Electrochemical Bioreceptor-free Biosensorsmentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

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Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

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Machine learning techniques for classification of breast tissue

Cited by 44 publications

References 27 publications

Discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties using machine learning methods

Discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties using machine learning methods

Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

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