{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T11:07:33Z","timestamp":1774436853636,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAPES-PRINT","award":["88881.310304\/2018-01"],"award-info":[{"award-number":["88881.310304\/2018-01"]}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","award":["E-26\/010.002431\/2019"],"award-info":[{"award-number":["E-26\/010.002431\/2019"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAPES PROEX","award":["23038.000949\/2018-15"],"award-info":[{"award-number":["23038.000949\/2018-15"]}]},{"name":"CNPq \/ INCT-MACC","award":["307329\/2016-0"],"award-info":[{"award-number":["307329\/2016-0"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.<\/jats:p>","DOI":"10.3390\/s21144802","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T10:13:42Z","timestamp":1626257622000},"page":"4802","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6068-8383","authenticated-orcid":false,"given":"Roger","family":"Resmini","sequence":"first","affiliation":[{"name":"Institute of Exact and Natural Sciences, Federal University of Rondon\u00f3polis, Cidade Universit\u00e1ria, Rondon\u00f3polis 78736-900, MT, Brazil"},{"name":"Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S\/N, Niter\u00f3i 24210-346, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3406-0146","authenticated-orcid":false,"given":"Lincoln","family":"Silva","sequence":"additional","affiliation":[{"name":"Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S\/N, Niter\u00f3i 24210-346, RJ, Brazil"},{"name":"Advanced Research Medical Laboratory, Departament of Information Technology and Education in Health, Faculty of Medical Sciences, State University of Rio de Janeiro, R. Professor Manuel de Abreu, 444, Rio de Janeiro 20550-170, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0637-8007","authenticated-orcid":false,"given":"Adriel S.","family":"Araujo","sequence":"additional","affiliation":[{"name":"Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S\/N, Niter\u00f3i 24210-346, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6052-6803","authenticated-orcid":false,"given":"Petrucio","family":"Medeiros","sequence":"additional","affiliation":[{"name":"M\u00eddiacom Lab, Institute of Computing, Fluminense Federal University, R. Passo da P\u00e1tria 156, Niter\u00f3i 24210-240, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1233-9736","authenticated-orcid":false,"given":"D\u00e9bora","family":"Muchaluat-Saade","sequence":"additional","affiliation":[{"name":"M\u00eddiacom Lab, Institute of Computing, Fluminense Federal University, R. Passo da P\u00e1tria 156, Niter\u00f3i 24210-240, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0782-2501","authenticated-orcid":false,"given":"Aura","family":"Conci","sequence":"additional","affiliation":[{"name":"Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S\/N, Niter\u00f3i 24210-346, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","unstructured":"International Agency for Research on Cancer (IARC) (2021). Cancer Tomorrow."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1016\/j.sigpro.2012.08.012","article-title":"Breast thermography from an image processing viewpoint: A survey","volume":"93","author":"Borchartt","year":"2013","journal-title":"Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","article-title":"Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential","volume":"31","author":"Doi","year":"2007","journal-title":"Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Arabi, P.M., Muttan, S., and Suji, R.J. (2010, January 23\u201325). Image enhancement for detection of early breast carcinoma by external irradiation. Proceedings of the 2010 Second International Conference on Computing, Communication and Networking Technologies, Bangkok, Thailand.","DOI":"10.1109\/ICCCNT.2010.5592580"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Damiao, C., Gonzalez, J.R., Moran, M.B.H., Fontes, C.P., Balarine, G., Cruz Filho, R., and Conci, A. (2020). On the possibility of using temperature to aid in thyroid nodule investigation. Sci. Rep., 2045\u20132322.","DOI":"10.1038\/s41598-020-78047-1"},{"key":"ref_7","first-page":"207","article-title":"Clinical Evaluation of Thermography and Heptyl Aldehyde in Breast Cancer Detection","volume":"19","author":"Vogler","year":"1959","journal-title":"Cancer Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1111\/j.1749-6632.1964.tb13704.x","article-title":"Thermography in Mass Screening for Breast Cancer","volume":"121","author":"Strax","year":"1964","journal-title":"Analls N. Y. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/1097-0142(196601)19:1<83::AID-CNCR2820190109>3.0.CO;2-6","article-title":"Thermography in the Detection of Breast Cancer","volume":"19","author":"Connell","year":"1966","journal-title":"Cancer"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Al Husaini, M.A.S., Habaebi, M.H., Hameed, S.A., Islam, M.R., and Gunawan, T.S. (2020). A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks. IEEE Access, 208922\u2013208937.","DOI":"10.1109\/ACCESS.2020.3038817"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.amjsurg.2008.06.015","article-title":"Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer","volume":"196","author":"Arora","year":"2008","journal-title":"Am. J. Surg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.ejso.2010.04.003","article-title":"The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy","volume":"36","author":"Wishart","year":"2010","journal-title":"Eur. J. Surg. Oncol. (EJSO)"},{"key":"ref_13","first-page":"265","article-title":"Diagnosis of Breast Cancer Using a Combination of Genetic Algorithm and Artificial Neural Network in Infrared Thermal Imaging","volume":"9","author":"Hossein","year":"2012","journal-title":"Iran. J. Med. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Krawczyk, B., Schaefer, G., and Wozniak, M. (2012, January 5\u20137). Breast thermogram analysis using a cost-sensitive multiple classifier system. Proceedings of the 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China.","DOI":"10.1109\/BHI.2012.6211629"},{"key":"ref_15","unstructured":"Opitz, D., and Maclin, R. (1997, January 12). An empirical evaluation of bagging and boosting for artificial neural networks. Proceedings of the 1997 IEEE International Conference on Neural Networks (ICNN 97), Houston, TX, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"68001","DOI":"10.1209\/0295-5075\/104\/68001","article-title":"Multifractal analysis of dynamic infrared imaging of breast cancer","volume":"104","author":"Gerasimova","year":"2014","journal-title":"Europhys. Lett. (EPL)"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gerasimova, E., Audit, B., Roux, S.G., Khalil, A., Gileva, O., Argoul, F., Naimark, O., and Arneodo, A. (2014). Wavelet-based multifractal analysis of dynamic infrared thermograms to assist in early breast cancer diagnosis. Front. Physiol., 5.","DOI":"10.3389\/fphys.2014.00176"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12","DOI":"10.4103\/2228-7477.175866","article-title":"Full Intelligent Cancer Classification of Thermal Breast Images to Assist Physician in Clinical Diagnostic Applications","volume":"6","author":"Lashkari","year":"2016","journal-title":"J. Med. Signals Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/17686733.2016.1176734","article-title":"An integrated index for breast cancer identification using histogram of oriented gradient and kernel locality preserving projection features extracted from thermograms","volume":"13","author":"Raghavendra","year":"2016","journal-title":"Quant. InfraRed Thermogr. J."},{"key":"ref_20","unstructured":"Santana, M., Pereira, J.M., Lima, N., Sousa, F., Lima, R., and Dos Santos, W. (2017). Classifica\u00e7\u00e3o de Les\u00f5es em Imagens Frontais de Termografia de Mama a Partir de Sistema Inteligente de Suporte ao Diagn\u00f3stico. I Simp\u00f3sio de Inova\u00e7\u00e3o em Engenharia Biom\u00e9dica (SABIO 2017), BioTech Consultoria."},{"key":"ref_21","first-page":"45","article-title":"Breast cancer diagnosis based on mammary thermography and extreme learning machines","volume":"34","author":"Santana","year":"2018","journal-title":"Int. J. Artif. Intell. Mach. Learn. (IJAIML)"},{"key":"ref_22","first-page":"1","article-title":"Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization","volume":"11","author":"Silva","year":"2021","journal-title":"Int. J. Artif. Intell. Mach. Learn. (IJAIML)"},{"key":"ref_23","unstructured":"Baffa, M.F.O., and Lattari, L.G. (November, January 29). Convolutional Neural Networks for Static and Dynamic Breast Infrared Imaging Classification. Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, Brazil."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rojas, I., Valenzuela, O., Rojas, F., and Ortu\u00f1o, F. (2019). Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. Bioinformatics and Biomedical Engineering, Springer International Publishing.","DOI":"10.1007\/978-3-030-17935-9"},{"key":"ref_25","first-page":"9807619","article-title":"Breast Cancer Identification via Thermography Image Segmentation with a Gradient Vector Flow and a Convolutional Neural Network","volume":"2019","author":"Woo","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1166\/jmihi.2014.1226","article-title":"A new database for breast research with infrared image","volume":"4","author":"Silva","year":"2014","journal-title":"J. Med Imaging Health Inform."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Silva, T.A.E., Silva, L.F., Muchaluat-Saade, D.C., and Conci, A. (2020). A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography. Sensors, 20.","DOI":"10.3390\/s20143866"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.patrec.2020.03.025","article-title":"Automatic region of interest segmentation for breast thermogram image classification","volume":"135","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","unstructured":"Mishra, V., and Rath, S.K. (2020). Detection of breast cancer tumours based on feature reduction and classification of thermograms. Quant. InfraRed Thermogr. J., 1\u201314."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","article-title":"Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy","volume":"51","author":"Kuncheva","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_31","unstructured":"Kuncheva, L., Bezdek, J.C., and Sutton, M.A. (1998, January 20\u201321). On combining multiple classifiers by fuzzy templates. Proceedings of the 1998 Conference of the North American Fuzzy Information Processing Society-NAFIPS (Cat. No. 98TH8353), Pensacola Beach, FL, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2014). Combining Pattern Classifiers: Methods and Algorithms, John Wiley & Sons.","DOI":"10.1002\/9781118914564"},{"key":"ref_33","unstructured":"Tan, P.N., Steinbach, M., and Kumar, V. (2016). Introduction to Data Mining, Pearson Education India."},{"key":"ref_34","unstructured":"da Gama, J.M.P. (1999). Combining Classification Algorithms. [Ph.D. Thesis, University of Porto]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.enbuild.2015.10.019","article-title":"Gated ensemble learning method for demand-side electricity load forecasting","volume":"109","author":"Burger","year":"2015","journal-title":"Energy Build."},{"key":"ref_36","first-page":"2267","article-title":"Data imbalance and classifiers: Impact and solutions from a big data perspective","volume":"13","author":"Madasamy","year":"2017","journal-title":"Int. J. Comput. Intell. Res."},{"key":"ref_37","unstructured":"Kuncheva, L.I., Matthews, C.E., Arnaiz-Gonz\u00e1lez, \u00c1., and Rodr\u00edguez, J.J. (2020). Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Resmini, R., Faria da Silva, L., Medeiros, P.R., Araujo, A.S., Muchaluat-Saade, D.C., and Conci, A. (2021). A Hybrid Methodology for Breast Screening and Cancer Diagnosis Using Thermography. Comput. Biol. Med., 104553.","DOI":"10.1016\/j.compbiomed.2021.104553"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kasabov, N.K. (1996). Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press.","DOI":"10.7551\/mitpress\/3071.001.0001"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1023\/B:MACH.0000015881.36452.6e","article-title":"Is combining classifiers better than selecting the best one?","volume":"54","author":"Dzeroski","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_42","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2005). Practical Machine Learning Tools and Techniques, Morgan Kaufmann."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","article-title":"A genetic algorithm tutorial","volume":"4","author":"Whitley","year":"1994","journal-title":"Stat. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"dos Santos Araujo, A., Resmini, R., Moran, M.B.H., de Sousa Issa, M.H., and Conci, A. (2021). Computer Techniques for Detection of Breast Cancer and Follow Up Neoadjuvant Treatment: Using Infrared Examinations. Biomedical Computing for Breast Cancer Detection and Diagnosis, IGI Global.","DOI":"10.4018\/978-1-7998-3456-4.ch005"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TIP.2010.2042645","article-title":"Enhanced local texture feature sets for face recognition under difficult lighting conditions","volume":"19","author":"Tan","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4415","DOI":"10.1109\/TSP.2007.896255","article-title":"Generalized Daubechies Wavelet Families","volume":"55","author":"Vonesch","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/0167-2789(88)90081-4","article-title":"Approach to an irregular time series on the basis of the fractal theory","volume":"31","author":"Higuchi","year":"1988","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_50","unstructured":"Petrosian, A. (1995, January 9\u201310). Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns. Proceedings of the Eighth IEEE Symposium on Computer-Based Medical Systems, Lubbock, TX, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1061\/TACEAT.0006518","article-title":"Long-term storage capacity of reservoirs","volume":"116","author":"Hurst","year":"1951","journal-title":"Trans. Am. Soc. Civ. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Huang, M.W., Chen, C.W., Lin, W.C., Ke, S.W., and Tsai, C.F. (2017). SVM and SVM ensembles in breast cancer prediction. PLOS ONE, 12.","DOI":"10.1371\/journal.pone.0161501"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","article-title":"Support vector machines combined with feature selection for breast cancer diagnosis","volume":"36","author":"Akay","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_55","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"scikit-image: Image processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_57","first-page":"89","article-title":"Meta-analysis of computational methods for breast cancer classification","volume":"13","author":"Pham","year":"2020","journal-title":"Int. J. Intell. Inf. Database Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.jmsy.2013.05.006","article-title":"Feature selection for manufacturing process monitoring using cross-validation","volume":"32","author":"Shao","year":"2013","journal-title":"J. Manuf. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4802\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:30:28Z","timestamp":1760164228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4802"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,14]]},"references-count":58,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144802"],"URL":"https:\/\/doi.org\/10.3390\/s21144802","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,14]]}}}