A novel TMGWO–SLBNC‐based multidimensional feature subset selection and classification framework for frequent diagnosis of breast lesion abnormalities

International Journal of Intelligent SystemsVolume 37, Issue 3 p. 2131-2162 RESEARCH ARTICLE A novel TMGWO–SLBNC-based multidimensional feature subset selection and classification framework for frequent diagnosis of breast lesion abnormalities Marwa M. Ahmed, Corresponding Author Marwa M. Ahmed mmhahmed@kau.edu.sa orcid.org/0000-0002-2291-4933 Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia Correspondence Marwa M. Ahmed, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Email: mmhahmed@kau.edu.saSearch for more papers by this authorThangam Palaniswamy, Thangam Palaniswamy orcid.org/0000-0003-1354-5811 Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi ArabiaSearch for more papers by this author Marwa M. Ahmed, Corresponding Author Marwa M. Ahmed mmhahmed@kau.edu.sa orcid.org/0000-0002-2291-4933 Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia Correspondence Marwa M. Ahmed, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Email: mmhahmed@kau.edu.saSearch for more papers by this authorThangam Palaniswamy, Thangam Palaniswamy orcid.org/0000-0003-1354-5811 Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi ArabiaSearch for more papers by this author First published: 02 December 2021 https://doi.org/10.1002/int.22768Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The selection of optimal subset of features from high-dimensional data sets still remains a major challenge during breast cancer detection and categorization. There exist several research works regarding optimal feature subset selection from high-dimensional data sets, but the obtained results are not satisfying when multidimensional data sets (MDDs) are employed in large amount during disease analysis. In this article, an effective feature subset selection and classification method suitable for MDD is proposed. At first, the important and distinct features are extracted from the mammogram images using a Deep Neural Network with wrapper-based extraction technique. Then, a novel two-phase mutation strategy integrated with grey wolf optimizer algorithm is employed for selecting the most relevant feature subsets. Finally, a learning-based semilazy Bayesian network classifier with parallel implementation is proposed for the precise categorization of the breast cancer stages. The proposed method is executed in MATLAB platform and analyzed using mammogram images taken from MAMMOSET database. The proposed method is likened with three state-of-the-art existing feature subset selection and classification approaches for validating the efficiency of the proposed approach. For Data set 1, the proposed method shows an accuracy of 90%, 92% and 98%, which is better than the existing methods taken for comparison. Also for Data set 2, the proposed method shows an accuracy of 97%, 91% and 94%, which is much better than the accuracy achieved by the existing approaches. Thus, the proposed approach outperforms the compared existing approaches by providing better precision, recall, F-measure, specificity and accuracy. CONFLICT OF INTERESTS The authors declare that there are no conflict of interests. Citing Literature Volume37, Issue3March 2022Pages 2131-2162 RelatedInformation