Breast Cancer diagnosis using, grey-level co-occurrence matrices, decision tree classification and evolutionary feature selection

Yaghoobi, Hanif; Barandagh, Alireza Ghahramani; Mohammadi, Zhila

doi:10.1109/kbei.2015.7436065

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Yaghoobi et al (8) diagnosed breast cancer by Gray Level Co-occurrence Matrix (GLCM) and cumulative histogram features. They used the texture attributes from GLCM and presented the ICA feature selection method, decision tree, and neural network for feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

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Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer

Masoudiasl

Vahdat

Hessam

et al. 2019

Iran Red Crescent Med J

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Background: Breast cancer is the most common cancer in women, which has not been completely cured yet. The traditional approaches have low accuracy for breast cancer detection. However, intelligent techniques have been recently used in medical research to distinguish infected individuals from healthy ones, accurately. Objectives: In this study, we aim to develop an ensemble machine learning (ML) method to distinguish tumor samples from healthy samples robustly. Methods: We used an Imperial Competitive Algorithm coupled with a Fuzzy System (ICA-Fuzzy-SR) to identify the most influencing features to recognize tumor samples. To evaluate the proposed method, we used the publicly available Wisconsin Breast Cancer Dataset (WBCD). Results: Benchmarking with the current existing leading methods indicates that our proposed method achieves 95.45% prediction accuracy, which is 3% better than those reported in previous studies. Conclusions: Such results achieve while our model is significantly faster than previously proposed models to solve this problem.

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Section: Literature Reviewmentioning

confidence: 99%

“…It has a better convergence rate compared to other optimization algorithms. Recently, ICA has been used in optimization algorithms, dimensionality reduction, and feature selection in many research topics such as medical research (8), (9), and (10).…”

Section: Introductionmentioning

confidence: 99%