A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breastcancer

Karakış, Rukiye; Tez, Mesut; Kilic, Yakup; Kuru, Y.; Güler, İnan

doi:10.1016/j.engappai.2012.10.013

Cited by 32 publications

(11 citation statements)

References 25 publications

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“…GA is a directed random search technique that is widely used to obtain the optimal solution for problems [16], [17]. Especially, it is benefit for complex optimization problems where the number of parameters is large and the analytical solutions are difficult to gain.…”

Section: A Brief Of Gamentioning

confidence: 99%

Genetic algorithm based on wavelet neural network for the displacement prediction of landslide

Jiang

Zeng

Chen

et al. 2014

2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI)

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A method to predict the displacement of landside, genetic algorithm based on wavelet neural network (GAWNN) model, is presented in this paper. The hybrid model improves the predicting precision, which is compared with genetic algorithm based on back-propagation neural network (GABPNN). Furthermore, the hybrid model is applied for predicting the displacement of Baishuihe landslides in the Three Gorges reservoir area of China. The result shows the better accuracy than GABPNN in terms of the same measurements.

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Section: A Brief Of Gamentioning

confidence: 99%

Genetic algorithm based on wavelet neural network for the displacement prediction of landslide

Jiang

Zeng

Chen

et al. 2014

2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI)

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“…In this paper, considering that the broad application prospects of GA with neural network [3][4][5][6]10], both BPNN and RBFN can be used as the approximate models in landslide forecasting with the GA method. Furthermore, take Yuhuangge landslide in the Three Gorges reservoir of China as a real case to test and verify these new frameworks.…”

Section: Introductionmentioning

confidence: 99%

“…Then, lots of improve algorithms to be proposed. Genetic Algorithm (GA) [9] with neural network [3][4][5][6]10] is very popular in forecasting and predicting. As we know, GA is effective in dealing with complex problems, which has been extensively applied in addressing the modeling and prediction problem of nonlinear time series.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm based Neural Network for the displacement of landslide forecasting

Chen

Zeng

Jiang

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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This paper proposes two hybrid prediction models using for predicting the displacement of landslide, Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFN) and Genetic Algorithm-Back Propagation Neural Network (GA-BPNN). A case study of Yuhuangge landslide in the Three Gorges reservoir in China is used to illustrate the capability and merit of our schemes. In addition, the result shows that GP-BPNN get better accuracy than GA-RBFN in the same measurements.

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“…Issac Niwas et al [16] extracted feature on fine needle aspiration cytology (FNAC) samples of breast tissue using complex wavelets, then the features extracted are used as input to a k-nn classifier, which obtained the correct classification results with 93.9 %. Karakıs et al [17] proposed a genetic algorithm model to predict the status of axillary lymph node which is important to assess metastatic breast cancer. In the study of Lacson et al [18], they presented the prevalence of each data element from breast imaging reports and automatically validated the extracted imaging findings compared to a Bgold standard^using manually extracted data from randomly selected reports.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Dong

Lǚ

et al. 2015

J Digit Imaging

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Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.

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A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breastcancer

Cited by 32 publications

References 25 publications

Genetic algorithm based on wavelet neural network for the displacement prediction of landslide

Genetic algorithm based on wavelet neural network for the displacement prediction of landslide

Genetic Algorithm based Neural Network for the displacement of landslide forecasting

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

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