Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Azar, Ahmad Taher; El-Said, Shaimaa Ahmed

doi:10.1007/s00521-012-1324-4

Cited by 189 publications

(63 citation statements)

References 39 publications

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“…A year after, another paper was published by the same group using PSO-SVM to analyse the WBCD data to achieve a 99.3% classification accuracy [87]. In 2014, five types of SVMs; proximal support vector machine (PSVM), finite Newton method for Lagrangian support vector machine (NSVM), linear programming support vector machine (LPSVM), Lagrangian support vector machine (LSVM), and smooth support vector machine (SSVM), were tested on WBCD data [88]. Overall accuracy was 97.20% in LPSVM, 96.60% in SSVM, 96.60% in NSVM, 96% in PSVM and 95.40% in LSVM.…”

Section: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

181

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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Section: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

181

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“…If T P (true positive) is the number of focal EEG signals identified as focal EEG signals, T N (true negative) is the number of non-focal EEG signals classified as non-focal EEG signals, F P (false positive) is the number of non-focal EEG signals recognized as focal EEG signals and F N (false negative) is the number of focal EEG signals distinguished as non-focal EEG signals, then the mathematical expressions of the performance measure parameters are as follows [62,63]:…”

Section: Performance Evaluationmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

283

175

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The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn Avg ), average Renyi's entropy (RenEn Avg ), average approximate entropy (ApEn Avg ), average sample entropy (SpEn Avg ) and average phase entropies (S1 Avg and S2 Avg ), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

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“…To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following equation set is often used in literature for examining performance quality as follows [32]: (22) where TP (true positive) represents the number of fatigue EEG signals identified as fatigue EEG signals; TN (true negative), the number of normal EEG signals classified as normal EEG signals; FP (false positive), the number of normal EEG signals recognized as fatigue EEG signals; FN (false negative), the number of fatigue EEG signals distinguished as normal EEG signals; Sn represents sensitivity; Sp represents specificity; Acc represents accuracy; and MCC represents Mathew's correlation coefficient.…”

Section: Performance Evaluationmentioning

confidence: 99%

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Zhang

Min

2017

Applied Sciences

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Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.

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Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Cited by 189 publications

References 39 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

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