{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T06:19:42Z","timestamp":1775369982644,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04692-w","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:20:09Z","timestamp":1771503609000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble Classification of Breast Cancer Using Texture and Color Features"],"prefix":"10.1007","volume":"7","author":[{"given":"Ankur Kumar","family":"Aggarwal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Alpana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mrinal","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"4692_CR1","unstructured":"Martin TA, Ye L, AJ S, Lane J, Jiang WG. Cancer invasion and metastasis: molecular and cellular perspective. Metastatic cancer clinical biological perspective; Madame Curie Bioscience Database. 2013;135\u201368. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK164700\/"},{"issue":"5","key":"4692_CR2","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.bpobgyn.2013.02.005","volume":"27","author":"B Lunenfeld","year":"2013","unstructured":"Lunenfeld B, Stratton P. The clinical consequences of an aging world and preventive strategies. Best Pract Res Clin Obstet Gynecol. 2013;27(5):643\u201359. https:\/\/doi.org\/10.1016\/j.bpobgyn.2013.02.005.","journal-title":"Best Pract Res Clin Obstet Gynecol"},{"issue":"3","key":"4692_CR3","doi-asserted-by":"publisher","first-page":"209","DOI":"10.5306\/wjco.v13.i3.209","volume":"13","author":"RM Yadav","year":"2022","unstructured":"Yadav RM. Breast cancer in india: present scenario and the challenges ahead. World J Clin Oncol. 2022;13(3):209\u201318. https:\/\/doi.org\/10.5306\/wjco.v13.i3.209.","journal-title":"World J Clin Oncol"},{"key":"4692_CR4","doi-asserted-by":"publisher","unstructured":"Jones GW. Delayed Marriage and Very Low Fertility in Pacific Asia. In Population and Development Revie, 33, pp. 453\u2013478 (2007). https:\/\/doi.org\/10.1111\/j.1728-4457.2007.00180.x","DOI":"10.1111\/j.1728-4457.2007.00180.x"},{"key":"4692_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.canep.2019.101660","volume":"64","author":"NP Oliveira","year":"2020","unstructured":"Oliveira NP, Siqueira CA, Lima KY, Cancela Md, Souza DL. Association of cervical and breast cancer mortality with socioeconomic indicators and availability of health services. Cancer Epidemiol. 2020;64:1\u20137. https:\/\/doi.org\/10.1016\/j.canep.2019.101660.","journal-title":"Cancer Epidemiol"},{"issue":"3","key":"4692_CR6","doi-asserted-by":"publisher","first-page":"461","DOI":"10.2214\/ajr.143.3.461","volume":"143","author":"E Sickles","year":"1984","unstructured":"Sickles E. Mammographic features of early breast cancer. Am J Roentgenol. 1984;143(3):461\u20134. https:\/\/doi.org\/10.2214\/ajr.143.3.461.","journal-title":"Am J Roentgenol"},{"key":"4692_CR7","doi-asserted-by":"publisher","unstructured":"Nover AB, Jagtap S, Anjum W, Yegingil H, Shih WY, Shih W-H, Brooks AD. Modern breast cancer detection: a technological review. Int J Biomed Imaging. 2009. https:\/\/doi.org\/10.1155\/2009\/902326.","DOI":"10.1155\/2009\/902326"},{"issue":"12","key":"4692_CR8","doi-asserted-by":"publisher","first-page":"5356","DOI":"10.1016\/j.eswa.2015.02.005","volume":"42","author":"H Lee","year":"2015","unstructured":"Lee H, Chen Y-PP. Image-based computer-aided diagnosis system for cancer detection. Expert Syst Appl. 2015;42(12):5356\u201365. https:\/\/doi.org\/10.1016\/j.eswa.2015.02.005.","journal-title":"Expert Syst Appl"},{"key":"4692_CR9","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ijthermalsci.2013.03.001","volume":"69","author":"M EtehadTavakol","year":"2013","unstructured":"EtehadTavakol M, Chandran V, Ng E, Kafieh R. Breast cancer detection from thermal images using bispectral invariant features. Int J Therm Sci. 2013;69:21\u201336. https:\/\/doi.org\/10.1016\/j.ijthermalsci.2013.03.001.","journal-title":"Int J Therm Sci"},{"key":"4692_CR10","doi-asserted-by":"publisher","unstructured":"Tan TZ, Quek C, Ng GS, Ng EYK. A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure. Expert Syst Appl. 2007;33(3):652\u201366. https:\/\/doi.org\/10.1016\/j.eswa.2006.06.012.","DOI":"10.1016\/j.eswa.2006.06.012"},{"key":"4692_CR11","doi-asserted-by":"publisher","unstructured":"Arakeri MP, Reddy GR. Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images. SIViP. 2013;9:409\u201325. https:\/\/doi.org\/10.1007\/s11760-013-0456-z.","DOI":"10.1007\/s11760-013-0456-z"},{"key":"4692_CR12","doi-asserted-by":"publisher","unstructured":"Etehadtavakol M, Ng EYK. Breast thermography as a potential Non-contact method in the early detection of cancer: a review. J Mech Med Biol. 2013;13(2):1\u201320. https:\/\/doi.org\/10.1142\/S0219519413300019","DOI":"10.1142\/S0219519413300019"},{"key":"4692_CR13","doi-asserted-by":"publisher","unstructured":"Soliman OO, Sweilam NH, Shawky DM. Automatic breast cancer detection using digital thermal images. Int Biomedical Eng Conf (CIBEC). 2018;110\u20133. https:\/\/doi.org\/10.1109\/CIBEC.2018.8641807.","DOI":"10.1109\/CIBEC.2018.8641807"},{"issue":"184","key":"4692_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-022-01536-9","volume":"4","author":"N Aidossov","year":"2023","unstructured":"Aidossov N, Zarikas V, Zhao Y, Mashekova A, Ng EY, Mukhmetov O, Omirbayev A. An integrated intelligent system for breast cancer detection at early stages using IR images and machine learning methods with explainability. SN Comput Sci. 2023;4(184):1\u201316. https:\/\/doi.org\/10.1007\/s42979-022-01536-9.","journal-title":"SN Comput Sci"},{"key":"4692_CR15","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s11265-022-01753-8","volume":"95","author":"SA Amin","year":"2023","unstructured":"Amin SA, Shanabari HA, Iqbal R, Karyotis C. An intelligent framework for automatic breast cancer classification using novel feature extraction and machine learning techniques. J Signal Process Syst. 2023;95:293\u2013303. https:\/\/doi.org\/10.1007\/s11265-022-01753-8.","journal-title":"J Signal Process Syst"},{"issue":"1","key":"4692_CR16","doi-asserted-by":"publisher","first-page":"49","DOI":"10.33545\/26633582.2022.v4.i1a.68","volume":"4","author":"VR Allugunti","year":"2022","unstructured":"Allugunti VR. Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. Int J Eng Comput Sci. 2022;4(1):49\u201356. https:\/\/doi.org\/10.33545\/26633582.2022.v4.i1a.68.","journal-title":"Int J Eng Comput Sci"},{"key":"4692_CR17","doi-asserted-by":"publisher","first-page":"103337","DOI":"10.1016\/j.jtherbio.2022.103337","volume":"110","author":"S Periyasamy","year":"2022","unstructured":"Periyasamy S, Prakasarao A, Menaka M, Venkatraman B, Jayashree M. Support vector machine-based methodology for classification of thermal images pertaining to breast cancer. J Therm Biol. 2022;110:103337. https:\/\/doi.org\/10.1016\/j.jtherbio.2022.103337.","journal-title":"J Therm Biol"},{"key":"4692_CR18","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1007\/s10044-021-00963-3","volume":"24","author":"R Karthiga","year":"2021","unstructured":"Karthiga R, Narasimhan K. Medical imaging technique using curvelet transform and machine learning for the automated diagnosis of breast cancer from thermal image. Pattern Anal Appl. 2021;24:981\u201391. https:\/\/doi.org\/10.1007\/s10044-021-00963-3.","journal-title":"Pattern Anal Appl"},{"key":"4692_CR19","doi-asserted-by":"publisher","first-page":"28303","DOI":"10.1007\/s11042-021-11082-w","volume":"80","author":"S Krishna","year":"2021","unstructured":"Krishna S, George B. An affordable solution for the recognition of abnormality in breast thermogram. Multimedia Tools Appl. 2021;80:28303\u201328. https:\/\/doi.org\/10.1007\/s11042-021-11082-w.","journal-title":"Multimedia Tools Appl"},{"key":"4692_CR20","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s42600-021-00158-z","volume":"37","author":"JM Pereira","year":"2021","unstructured":"Pereira JM, Santana MA, Gomes JC, Barbosa VA, Valen\u00e7a MJ, Lima SM, Santos WP. Feature selection based on dialectics to support breast cancer diagnosis using thermographic images. Res Biomed Eng. 2021;37:485\u2013506. https:\/\/doi.org\/10.1007\/s42600-021-00158-z.","journal-title":"Res Biomed Eng"},{"key":"4692_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2020.101854","volume":"105","author":"ST Kakileti","year":"2020","unstructured":"Kakileti ST, Madhu HJ, Manjunath G, Wee L, Dekker A, Sampangi S. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics. Artif Intell Med. 2020;105:1\u20138. https:\/\/doi.org\/10.1016\/j.artmed.2020.101854.","journal-title":"Artif Intell Med"},{"issue":"2","key":"4692_CR22","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3390\/app10020551","volume":"10","author":"F AlFayez","year":"2020","unstructured":"AlFayez F, El-Soud MW, Gaber T. Thermogram breast cancer detection: a comparative study of two machine learning techniques. Appl Sci. 2020;10(2):551. https:\/\/doi.org\/10.3390\/app10020551.","journal-title":"Appl Sci"},{"key":"4692_CR23","doi-asserted-by":"publisher","unstructured":"Sengar PP, Gaikwad MJ, Nagdive AS. Comparative study of machine learning algorithms for breast cancer prediction. Third Int Conf Smart Syst Inventive Technol. 2020. https:\/\/doi.org\/10.1109\/ICSSIT48917.2020.9214267.","DOI":"10.1109\/ICSSIT48917.2020.9214267"},{"key":"4692_CR24","doi-asserted-by":"publisher","unstructured":"Iqbal HT, Majeed B, Khan U, Altaf MA. An infrared high classification accuracy hand-held machine learning based breast-cancer detection system. In: 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS); 2019. pp. 1\u20134. IEEE. https:\/\/doi.org\/10.1109\/BIOCAS.2019.8918687","DOI":"10.1109\/BIOCAS.2019.8918687"},{"key":"4692_CR25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.21595\/vp.2019.20978","volume":"26","author":"J Ma","year":"2019","unstructured":"Ma J, Shang P, Lu C, Meraghni S, Benaggoune K, Zuluaga J, Masry ZA. A portable breast cancer detection system based on smartphone with infrared camera. Vibroeng PROCEDIA. 2019;26:57\u201363. https:\/\/doi.org\/10.21595\/vp.2019.20978.","journal-title":"Vibroeng PROCEDIA"},{"issue":"1","key":"4692_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics8010100","volume":"8","author":"M Abdel-Nasser","year":"2019","unstructured":"Abdel-Nasser M, Moreno A, Puig D. Breast cancer detection in thermal infrared images using representation learning and texture analysis methods. Electronics. 2019;8(1):1\u201318. https:\/\/doi.org\/10.3390\/electronics8010100.","journal-title":"Electronics"},{"key":"4692_CR27","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1007\/s13246-018-0681-4","volume":"41","author":"UR Gogoi","year":"2018","unstructured":"Gogoi UR, Bhowmik MK, Bhattacharjee D, Ghosh AK. Singular value based characterization and analysis of thermal patches for early breast abnormality detection. Australasian Phys Eng Sci Med. 2018;41:861\u201379. https:\/\/doi.org\/10.1007\/s13246-018-0681-4.","journal-title":"Australasian Phys Eng Sci Med"},{"issue":"3","key":"4692_CR28","first-page":"35","volume":"10","author":"D Sathish","year":"2018","unstructured":"Sathish D, Kamath S. Detection of breast thermograms using ensemble classifiers. J Telecommun Electron Comput Eng. 2018;10(3):35\u20139.","journal-title":"J Telecommun Electron Comput Eng"},{"issue":"1","key":"4692_CR29","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1590\/2446-4740.05217","volume":"34","author":"MA Santana","year":"2018","unstructured":"Santana MA, Pereira JM, Silva FL, Lima NM, Sousa FN, Arruda GM, Santos WP. Breast cancer diagnosis based on mammary thermography and extreme learning machines. Res Biomed Eng. 2018;34(1):45\u201353. https:\/\/doi.org\/10.1590\/2446-4740.05217.","journal-title":"Res Biomed Eng"},{"key":"4692_CR30","doi-asserted-by":"publisher","unstructured":"Gogoi UR, Bhowmik MK, Ghosh AK, Bhattacharjee D, Majumdar G. Discriminative feature selection for breast abnormality detection and accurate classification of thermograms. Int Conf Innov Electron Signal Process Commun (IESC). 2017. https:\/\/doi.org\/10.1109\/IESPC.2017.8071861.","DOI":"10.1109\/IESPC.2017.8071861"},{"issue":"2","key":"4692_CR31","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.3837\/tiis.2017.02.029","volume":"11","author":"S Min","year":"2017","unstructured":"Min S, Heo J, Kong Y, Nam Y, Ley P, Jung B-K, Shin W. Thermal infrared image analysis for breast cancer detection. KSII Trans Internet Inf Syst. 2017;11(2):1134\u201347. https:\/\/doi.org\/10.3837\/tiis.2017.02.029.","journal-title":"KSII Trans Internet Inf Syst"},{"key":"4692_CR32","doi-asserted-by":"publisher","unstructured":"Pramanik S, Bhattacharjee D, Nasipuri M. Texture analysis of breast thermogram for differentiation of malignant and benign breast. International Conference on Advances in Computing, Communications and Informatics (ICACCI); 2016, pp. 8\u201314. https:\/\/doi.org\/10.1109\/ICACCI.2016.7732018","DOI":"10.1109\/ICACCI.2016.7732018"},{"key":"4692_CR33","doi-asserted-by":"publisher","unstructured":"Yadav P, Jethani V. Breast thermograms analysis for cancer detection using feature extraction and data mining technique. International Conference on Advances in Information Communication Technology & Computing; 2016, pp. 1\u20135. https:\/\/doi.org\/10.1145\/2979779.2979866","DOI":"10.1145\/2979779.2979866"},{"key":"4692_CR34","doi-asserted-by":"publisher","unstructured":"Gaber T, Ismail G, Anter A, Soliman M, Ali M, Semary N, Snasel V. Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy c-means algorithm. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015, pp. 4254\u20137. https:\/\/doi.org\/10.1109\/EMBC.2015.7319334","DOI":"10.1109\/EMBC.2015.7319334"},{"key":"4692_CR35","doi-asserted-by":"publisher","unstructured":"Pramanik S, Bhattacharjee D, Nasipuri M. Wavelet based thermogram analysis for breast cancer detection. International Symposium on Advanced Computing and Communication (ISACC); 2015, pp. 205\u201312. https:\/\/doi.org\/10.1109\/ISACC.2015.7377343","DOI":"10.1109\/ISACC.2015.7377343"},{"key":"4692_CR36","doi-asserted-by":"publisher","unstructured":"Gogoi UR, Majumdar G, Bhowmik MK, Ghosh AK, Bhattacharjee D. Breast abnormality detection through statistical feature analysis using infrared thermograms. Int Symp Adv Comput Communication (ISACC). 2015. https:\/\/doi.org\/10.1109\/ISACC.2015.7377351.","DOI":"10.1109\/ISACC.2015.7377351"},{"key":"4692_CR37","first-page":"1204","volume":"13","author":"M Milosevic","year":"2014","unstructured":"Milosevic M, Jankovic D, Peulic A. Thermography based breast cancer detection using texture features and minimum variance quantization. EXCLI J. 2014;13:1204\u201315.","journal-title":"EXCLI J"},{"key":"4692_CR38","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.infrared.2014.08.019","volume":"67","author":"SV Francis","year":"2014","unstructured":"Francis SV, Bharathi MS, G., Jaipurkar D. Breast cancer detection in rotational thermography images using texture features. Infrared Phys Technol. 2014;67:490\u20136. https:\/\/doi.org\/10.1016\/j.infrared.2014.08.019.","journal-title":"Infrared Phys Technol"},{"issue":"15","key":"4692_CR39","doi-asserted-by":"publisher","first-page":"6728","DOI":"10.1016\/j.eswa.2014.04.027","volume":"41","author":"M C.Ara\u00fajo","year":"2014","unstructured":"C.Ara\u00fajo M, Lima CF, R., Souza RM. Interval symbolic feature extraction for thermography breast cancer detection. Expert Syst Appl. 2014;41(15):6728\u201337. https:\/\/doi.org\/10.1016\/j.eswa.2014.04.027.","journal-title":"Expert Syst Appl"},{"key":"4692_CR40","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1007\/s10916-010-9611-z","volume":"36","author":"UR Acharya","year":"2012","unstructured":"Acharya UR, Ng EY, Tan J-H, Sree SV. Thermography based breast cancer detection using texture features and support vector machine. J Med Syst. 2012;36:1503\u201310. https:\/\/doi.org\/10.1007\/s10916-010-9611-z.","journal-title":"J Med Syst"},{"issue":"2","key":"4692_CR41","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1080\/17686733.2012.738788","volume":"9","author":"MR Mookiah","year":"2012","unstructured":"Mookiah MR, U. R., Ng E. Data mining technique for breast cancer detection in thermograms using hybrid feature extraction strategy. Quant InfraRed Thermography J. 2012;9(2):151\u201365. https:\/\/doi.org\/10.1080\/17686733.2012.738788.","journal-title":"Quant InfraRed Thermography J"},{"issue":"1","key":"4692_CR42","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/j.1468-0394.2012.00654.x","volume":"31","author":"UR Acharya","year":"2012","unstructured":"Acharya UR, Ng E, Sree SV, Chu CK. Higher order spectra analysis of breast thermograms for the automated identification of breast cancer. Expert Syst. 2012;31(1):37\u201347. https:\/\/doi.org\/10.1111\/j.1468-0394.2012.00654.x.","journal-title":"Expert Syst"},{"issue":"2","key":"4692_CR43","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1080\/03091900600562040","volume":"32","author":"EK Ng","year":"2008","unstructured":"Ng EK, Kee EC. Advanced integrated technique in breast cancer thermography. J Med Eng Technol. 2008;32(2):103\u201314. https:\/\/doi.org\/10.1080\/03091900600562040.","journal-title":"J Med Eng Technol"},{"issue":"1","key":"4692_CR44","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1166\/jmihi.2014.1226","volume":"4","author":"LF Silva","year":"2014","unstructured":"Silva LF, Saade DC, Olivera GO, Silva A, Paiva A, Bravo R, Conci A. A new database for breast research with infrared image. J Med Imaging Health Inf. 2014;4(1):92\u2013100. https:\/\/doi.org\/10.1166\/jmihi.2014.1226.","journal-title":"J Med Imaging Health Inf"},{"key":"4692_CR45","first-page":"231","volume":"7","author":"A Krogh","year":"1995","unstructured":"Krogh A, Vedelsby J. Neural network ensembles, cross validation and active learning. Adv Neural Inf Process Syst. 1995;7:231\u20138.","journal-title":"Adv Neural Inf Process Syst"},{"key":"4692_CR46","doi-asserted-by":"publisher","unstructured":"IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology, IEEE Std 610.4\u20131990; 1990, pp. 1\u201316. https:\/\/doi.org\/10.1109\/IEEESTD.1990.94600","DOI":"10.1109\/IEEESTD.1990.94600"},{"key":"4692_CR47","doi-asserted-by":"publisher","unstructured":"Stricker MA, Orengo M. Similarity of color images. Storage and retrieval for image and video databases III. (1995) https:\/\/doi.org\/10.1117\/12.205308","DOI":"10.1117\/12.205308"},{"issue":"5","key":"4692_CR48","first-page":"42","volume":"8","author":"F Alamdar","year":"2011","unstructured":"Alamdar F, Keyvanpour M. A new color feature extraction method based on dynamic color distribution entropy of neighborhoods. IJCSI Int J Comput Sci Issues. 2011;8(5):42\u20138.","journal-title":"IJCSI Int J Comput Sci Issues"},{"issue":"1","key":"4692_CR49","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.chemolab.2004.02.005","volume":"72","author":"H Manish","year":"2004","unstructured":"Manish H, Bharati JL. Image texture analysis: methods and comparisons. Chemometr Intell Lab Syst. 2004;72(1):57\u201371. https:\/\/doi.org\/10.1016\/j.chemolab.2004.02.005.","journal-title":"Chemometr Intell Lab Syst"},{"issue":"1\u20134","key":"4692_CR50","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.minpro.2011.07.008","volume":"101","author":"CA Perez","year":"2011","unstructured":"Perez CA, Est\u00e9vez PA, Vera PA, Castillo LE, Aravena CM, Schulz DA, Medina LE. Ore grade estimation by feature selection and voting using boundary detection in digital image analysis. Int J Miner Process. 2011;101(1\u20134):28\u201336. https:\/\/doi.org\/10.1016\/j.minpro.2011.07.008.","journal-title":"Int J Miner Process"},{"issue":"6","key":"4692_CR51","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3(6):610\u201321. https:\/\/doi.org\/10.1109\/TSMC.1973.4309314.","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"4692_CR52","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.compind.2015.10.003","volume":"75","author":"Alpana","year":"2016","unstructured":"Alpana, Mohapatra S. Machine learning approach for automated coal characterization using scanned electron microscopic images. Comput Ind. 2016;75:35\u201345. https:\/\/doi.org\/10.1016\/j.compind.2015.10.003.","journal-title":"Comput Ind"},{"issue":"2","key":"4692_CR53","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1504\/IJOGCT.2021.117441","volume":"28","author":"Alpana","year":"2021","unstructured":"Alpana, Chand S, Mohapatra S, Mishra V. Automated char classification using image analysis and artificial intelligence. Int J Oil Gas Coal Technol (IJOGCT). 2021;28(2):235\u201348.","journal-title":"Int J Oil Gas Coal Technol (IJOGCT)"},{"issue":"87","key":"4692_CR54","doi-asserted-by":"publisher","first-page":"232","DOI":"10.19101\/IJATEE.2021.874555","volume":"9","author":"B Thakur","year":"2022","unstructured":"Thakur B, Kumar N, Gupta G. Machine learning techniques with ANOVA for the prediction of breast cancer. Int J Adv Technol Eng Explor (IJATEE). 2022;9(87):232\u201345. https:\/\/doi.org\/10.19101\/IJATEE.2021.874555.","journal-title":"Int J Adv Technol Eng Explor (IJATEE)"},{"key":"4692_CR55","unstructured":"Conner-Simons A. Using artificial intelligence to improve early breast cancer detection. https:\/\/news.mit.edu\/2017\/artificial-intelligence-early-breast-cancer-detection-1017. Accessed 30 Mar 2023."},{"issue":"10","key":"4692_CR56","doi-asserted-by":"publisher","first-page":"16","DOI":"10.5120\/17219-7456","volume":"98","author":"R Sumbaly","year":"2014","unstructured":"Sumbaly R, Vishnusri N, Jeyalatha S. Diagnosis of breast cancer using decision tree data mining technique. Int J Comput Appl. 2014;98(10):16\u201324. https:\/\/doi.org\/10.5120\/17219-7456.","journal-title":"Int J Comput Appl"},{"issue":"3","key":"4692_CR57","first-page":"1","volume":"5","author":"EE Ali","year":"2016","unstructured":"Ali EE, Feng WZ. Breast cancer classification using support vector machine and neural network. Int J Sci Res. 2016;5(3):1\u20136.","journal-title":"Int J Sci Res"},{"issue":"1","key":"4692_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5120\/10041-4635","volume":"62","author":"SA Medjahed","year":"2013","unstructured":"Medjahed SA, Saadi TA. Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. Int J Comput Appl. 2013;62(1):1\u20135. https:\/\/doi.org\/10.5120\/10041-4635.","journal-title":"Int J Comput Appl"},{"issue":"3","key":"4692_CR59","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst Mag. 2006;6(3):21\u201345. https:\/\/doi.org\/10.1109\/MCAS.2006.1688199.","journal-title":"IEEE Circuits Syst Mag"},{"key":"4692_CR60","doi-asserted-by":"publisher","unstructured":"Kuncheva LI. Combining pattern classifiers: methods and algorithms. Wiley Online; 2004. https:\/\/doi.org\/10.1002\/0471660264.","DOI":"10.1002\/0471660264"},{"key":"4692_CR61","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-319-71249-9_9","volume":"10534","author":"M Cliche","year":"2017","unstructured":"Cliche M, Rosenberg D, Madeka D, Yee C. Scatteract: automated extraction of data from scatter plots. Joint Eur Conf Mach Learn Knowl Discovery Databases. 2017;10534:135\u201350. https:\/\/doi.org\/10.1007\/978-3-319-71249-9_9.","journal-title":"Joint Eur Conf Mach Learn Knowl Discovery Databases"},{"issue":"4","key":"4692_CR62","first-page":"75","volume":"6","author":"NJR Maria","year":"2016","unstructured":"Maria NJR, Pankaja R. Performance analysis of text classification algorithms using confusion matrix. Int J Eng Tech Res (IJETR). 2016;6(4):75\u20138.","journal-title":"Int J Eng Tech Res (IJETR)"}],"updated-by":[{"DOI":"10.1007\/s42979-026-04879-9","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:00:00Z","timestamp":1775260800000}}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04692-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04692-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04692-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:35:25Z","timestamp":1775367325000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04692-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":62,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["4692"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04692-w","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]},"assertion":[{"value":"11 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised due to family name of the author Alpana was incorrectly published as Alphana. Now, the author name has been corrected.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2026","order":8,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":9,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":10,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s42979-026-04879-9","URL":"https:\/\/doi.org\/10.1007\/s42979-026-04879-9","order":11,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and\/or Animals"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"221"}}