{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T01:22:28Z","timestamp":1773364948805,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00521-023-08992-1","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T13:01:56Z","timestamp":1695214916000},"page":"7909-7924","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5697-3659","authenticated-orcid":false,"given":"Debendra","family":"Muduli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rakesh Ranjan","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jitesh","family":"Pradhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhinav","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"8992_CR1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.engappai.2016.12.019","volume":"59","author":"M Abdel-Nasser","year":"2017","unstructured":"Abdel-Nasser M, Melendez J, Moreno A, Omer OA, Puig D (2017) Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell 59:84\u201392","journal-title":"Eng Appl Artif Intell"},{"key":"8992_CR2","first-page":"1050","volume":"5","author":"M Abdul Salam","year":"2015","unstructured":"Abdul Salam M (2015) FPA-ELM model for stock market prediction. Int J Adv Res Comput Sci Softw Engi 5:1050\u20131063","journal-title":"Int J Adv Res Comput Sci Softw Engi"},{"key":"8992_CR3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2018.11.006","volume":"169","author":"A Ahmadi","year":"2019","unstructured":"Ahmadi A, Davoudi S, Daliri MR (2019) Computer aided diagnosis system for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention. Comput Methods Programs Biomed 169:9\u201318","journal-title":"Comput Methods Programs Biomed"},{"key":"8992_CR4","doi-asserted-by":"crossref","unstructured":"Ban X, Liu R, Shen Q, Wang Y (2016) Weighted extreme learning machine for balance and optimization learning. In: 2016 4th International conference on cloud computing and intelligence systems (CCIS), pp 6\u201310","DOI":"10.1109\/CCIS.2016.7790215"},{"issue":"1","key":"8992_CR5","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s13538-011-0052-z","volume":"42","author":"JCA Barata","year":"2012","unstructured":"Barata JCA, Hussein MS (2012) The Moore-Penrose pseudoinverse: a tutorial review of the theory. Braz J Phys 42(1):146\u2013165","journal-title":"Braz J Phys"},{"key":"8992_CR6","unstructured":"Bhusri S, Jain S, Virmani J (2016) Classification of breast lesions using the difference of statistical features. 7:1365\u20131372"},{"key":"8992_CR7","unstructured":"Bhusri S, Jain S, Virmani J (2017) Classification of breast lesions using texture ratio vector technique. In: Proceedings of the 11th INDIACom international conference on computing for sustainable global development, pp 6289\u20136293"},{"issue":"6","key":"8992_CR8","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2022","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2022) Global cancer statistics 2022: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394\u2013424","journal-title":"CA Cancer J Clin"},{"key":"8992_CR9","unstructured":"Broomhead D, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal signals and radar establishment malvern (United Kingdom) RSRE-MEMO-4148"},{"issue":"1","key":"8992_CR10","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ins.2011.09.015","volume":"185","author":"J Cao","year":"2012","unstructured":"Cao J, Lin Z, Huang GB, Liu N (2012) Voting based extreme learning machine. Inf Sci 185(1):66\u201377","journal-title":"Inf Sci"},{"issue":"5","key":"8992_CR11","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/S0301-5629(02)00788-3","volume":"29","author":"RF Chang","year":"2003","unstructured":"Chang RF, Wu WJ, Moon WK, Chen DR (2003) Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med Biol 29(5):679\u2013686","journal-title":"Ultrasound Med Biol"},{"issue":"2","key":"8992_CR12","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S1076-6332(03)80044-2","volume":"10","author":"RF Chang","year":"2003","unstructured":"Chang RF, Wu WJ, Moon WK, Chou YH, Chen DR (2003) Support vector machines for diagnosis of breast tumors on us images. Acad Radiol 10(2):189\u2013197","journal-title":"Acad Radiol"},{"issue":"2","key":"8992_CR13","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1148\/radiology.213.2.r99nv13407","volume":"213","author":"DR Chen","year":"1999","unstructured":"Chen DR, Chang RF, Huang YL (1999) Computer-aided diagnosis applied to us of solid breast nodules by using neural networks. Radiology 213(2):407\u2013412","journal-title":"Radiology"},{"issue":"12","key":"8992_CR14","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1016\/S0031-3203(03)00192-4","volume":"36","author":"H Cheng","year":"2003","unstructured":"Cheng H, Cai X, Chen X, Hu L, Lou X (2003) Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn 36(12):2967\u20132991","journal-title":"Pattern Recogn"},{"issue":"1","key":"8992_CR15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.patcog.2009.05.012","volume":"43","author":"H Cheng","year":"2010","unstructured":"Cheng H, Shan J, Ju W, Guo Y, Zhang L (2010) Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn 43(1):299\u2013317","journal-title":"Pattern Recogn"},{"issue":"1","key":"8992_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0001-8","volume":"6","author":"JZ Cheng","year":"2016","unstructured":"Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D, Chen CM (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans. Sci Rep 6(1):1\u201313","journal-title":"Sci Rep"},{"key":"8992_CR17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neunet.2015.04.002","volume":"68","author":"D Chyzhyk","year":"2015","unstructured":"Chyzhyk D, Savio A, Gra\u00f1a M (2015) Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Netw 68:23\u201333","journal-title":"Neural Netw"},{"key":"8992_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11042-018-6670-5","volume":"78","author":"D Das","year":"2019","unstructured":"Das D, Nayak D, Dash R, Majhi B (2019) An empirical evaluation of extreme learning machine: application to handwritten character recognition. Multimed Tools Appl 78:1\u201329","journal-title":"Multimed Tools Appl"},{"key":"8992_CR19","first-page":"249","volume":"9","author":"X Glorot","year":"2010","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res Proc Track 9:249\u2013256","journal-title":"J Mach Learn Res Proc Track"},{"issue":"19","key":"8992_CR20","doi-asserted-by":"crossref","first-page":"7714","DOI":"10.1088\/1361-6560\/aa82ec","volume":"62","author":"S Han","year":"2017","unstructured":"Han S, Kang HK, Jeong JY, Park MH, Kim W, Bang WC, Seong YK (2017) A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 62(19):7714\u20137728","journal-title":"Phys Med Biol"},{"key":"8992_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8992_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8992_CR23","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neucom.2013.08.024","volume":"128","author":"YL He","year":"2014","unstructured":"He YL, Geng ZQ, Xu Y, Zhu QX (2014) A hierarchical structure of extreme learning machine (helm) for high-dimensional datasets with noise. Neurocomputing 128:407\u2013414","journal-title":"Neurocomputing"},{"issue":"2","key":"8992_CR24","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G Huang","year":"2012","unstructured":"Huang G, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst, Man, and Cybern, Part B (Cybern) 42(2):513\u2013529","journal-title":"IEEE Trans Syst, Man, and Cybern, Part B (Cybern)"},{"key":"8992_CR25","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"8992_CR26","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"issue":"3","key":"8992_CR27","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.clinimag.2004.08.002","volume":"29","author":"YL Huang","year":"2005","unstructured":"Huang YL, Chen DR (2005) Support vector machines in sonography: application to decision making in the diagnosis of breast cancer. Clin Imag 29(3):179\u2013184","journal-title":"Clin Imag"},{"issue":"2","key":"8992_CR28","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1007\/s00521-005-0019-5","volume":"15","author":"YL Huang","year":"2006","unstructured":"Huang YL, Wang KL, Chen DR (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15(2):164\u2013169","journal-title":"Neural Comput Appl"},{"issue":"3","key":"8992_CR29","doi-asserted-by":"crossref","first-page":"807","DOI":"10.3390\/s22030807","volume":"22","author":"K Jabeen","year":"2022","unstructured":"Jabeen K, Khan MA, Alhaisoni M, Tariq U, Zhang YD, Hamza A, Mickus A, Dama\u0161evi\u010dius R (2022) Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors 22(3):807","journal-title":"Sensors"},{"issue":"4","key":"8992_CR30","first-page":"111","volume":"1","author":"B Karlik","year":"2011","unstructured":"Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst 1(4):111\u2013122","journal-title":"Int J Artif Intell Expert Syst"},{"issue":"8","key":"8992_CR31","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1109\/TIP.2016.2570569","volume":"25","author":"LLC Kasun","year":"2016","unstructured":"Kasun LLC, Yang Y, Huang G, Zhang Z (2016) Dimension reduction with extreme learning machine. IEEE Trans Image Process 25(8):3906\u20133918","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"8992_CR32","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.bbe.2019.02.004","volume":"39","author":"Virmani J Kriti","year":"2019","unstructured":"Kriti Virmani J, Agarwal R (2019) Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 39(2):536\u2013560","journal-title":"Biocybern Biomed Eng"},{"key":"8992_CR33","doi-asserted-by":"crossref","unstructured":"Kriti D, Virmani J, Agarwal R (2021) A characterization approach for the review of cad systems designed for breast tumor classification using b-mode ultrasound images. Arch Comput Methods Eng 29","DOI":"10.1007\/s11831-021-09620-8"},{"key":"8992_CR34","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"8992_CR35","doi-asserted-by":"crossref","unstructured":"Lee CY, Chen GL, Zhang ZX, Chou YH, Hsu CC (2018) Is intensity inhomogeneity correction useful for classification of breast cancer in sonograms using deep neural network?. J Healthc Eng","DOI":"10.1155\/2018\/8413403"},{"issue":"5","key":"8992_CR36","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.1007\/s10916-012-9825-3","volume":"36","author":"LN Li","year":"2012","unstructured":"Li LN, Ouyang JH, Chen HL, Liu DY (2012) A computer aided diagnosis system for thyroid disease using extreme learning machine. J Med Syst 36(5):3327\u20133337","journal-title":"J Med Syst"},{"key":"8992_CR37","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.neucom.2005.03.002","volume":"68","author":"MB Li","year":"2005","unstructured":"Li MB, Huang GB, Saratchandran P, Sundararajan N (2005) Fully complex extreme learning machine. Neurocomputing 68:306\u2013314","journal-title":"Neurocomputing"},{"issue":"6","key":"8992_CR38","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"NY Liang","year":"2006","unstructured":"Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411\u20131423","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"8992_CR39","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/TFUZZ.2013.2269149","volume":"22","author":"C Lin","year":"2014","unstructured":"Lin C, Hou Y, Chen T, Chen K (2014) Breast nodules computer-aided diagnostic system design using fuzzy cerebellar model neural networks. IEEE Trans Fuzzy Syst 22(3):693\u2013699","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"9","key":"8992_CR40","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.compbiomed.2012.07.001","volume":"42","author":"E Malar","year":"2012","unstructured":"Malar E, Kandaswamy A, Chakravarthy D, Dharan AG (2012) A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine. Comput Biol Med 42(9):898\u2013905","journal-title":"Comput Biol Med"},{"issue":"101","key":"8992_CR41","first-page":"912","volume":"59","author":"Dash R Muduli","year":"2020","unstructured":"Muduli Dash R, Majhi B (2020) Automated breast cancer detection in digital mammograms: a moth flame optimization based elm approach. Biomed Signal Process Control 59(101):912","journal-title":"Biomed Signal Process Control"},{"issue":"102","key":"8992_CR42","first-page":"919","volume":"70","author":"D Muduli","year":"2021","unstructured":"Muduli D, Dash R, Majhi B (2021) Fast discrete curvelet transform and modified PSO based improved evolutionary extreme learning machine for breast cancer detection. Biomed Signal Process Control 70(102):919","journal-title":"Biomed Signal Process Control"},{"issue":"102","key":"8992_CR43","first-page":"825","volume":"71","author":"D Muduli","year":"2022","unstructured":"Muduli D, Dash R, Majhi B (2022) Automated diagnosis of breast cancer using multi-modal datasets: a deep convolution neural network based approach. Biomed Signal Process Control 71(102):825","journal-title":"Biomed Signal Process Control"},{"key":"8992_CR44","unstructured":"Nixon M, Aguado A (2002) Feature extraction and image processing, newnes. Elsevier) Linacre House, Jordan Hill, Oxford OX28DP 30:747\u2013757"},{"issue":"106","key":"8992_CR45","first-page":"018","volume":"203","author":"T Pang","year":"2021","unstructured":"Pang T, Wong JHD, Ng WL, Chan CS (2021) Semi-supervised gan-based radiomics model for data augmentation in breast ultrasound mass classification. Comput Methods Programs Biomed 203(106):018","journal-title":"Comput Methods Programs Biomed"},{"key":"8992_CR46","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.media.2018.12.006","volume":"52","author":"X Qi","year":"2019","unstructured":"Qi X, Zhang L, Chen Y, Pi Y, Chen Y, Lv Q, Yi Z (2019) Automated diagnosis of breast ultrasonography images using deep neural networks. Med Image Anal 52:185\u2013198","journal-title":"Med Image Anal"},{"key":"8992_CR47","doi-asserted-by":"publisher","DOI":"10.17632\/wmy84gzngw.1","author":"P Rodrigues","year":"2017","unstructured":"Rodrigues P (2017) Breast ultrasound image. Mendeley Data. https:\/\/doi.org\/10.17632\/wmy84gzngw.1","journal-title":"Mendeley Data"},{"issue":"104","key":"8992_CR48","first-page":"292","volume":"80","author":"A Sahu","year":"2023","unstructured":"Sahu A, Das PK, Meher S (2023) High accuracy hybrid cnn classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomed Signal Process Control 80(104):292","journal-title":"Biomed Signal Process Control"},{"key":"8992_CR49","doi-asserted-by":"crossref","unstructured":"Segyeong Joo, Woo Kyung Moon, Hee Chan Kim (2004) Computer-aided diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol\u00a01, pp 1397\u20131400","DOI":"10.1109\/IEMBS.2004.1403434"},{"key":"8992_CR50","doi-asserted-by":"crossref","first-page":"32,804","DOI":"10.1109\/ACCESS.2023.3258549","volume":"11","author":"SK Sharma","year":"2023","unstructured":"Sharma SK, Zamani AT, Abdelsalam A, Muduli D, Alabrah AA, Parveen N, Alanazi SM (2023) A diabetes monitoring system and health-medical service composition model in cloud environment. IEEE Access 11:32,804-32,819","journal-title":"IEEE Access"},{"key":"8992_CR51","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neucom.2016.01.074","volume":"194","author":"J Shi","year":"2016","unstructured":"Shi J, Zhou S, Liu X, Zhang Q, Lu M, Wang T (2016) Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing 194:87\u201394","journal-title":"Neurocomputing"},{"key":"8992_CR52","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.eswa.2016.09.006","volume":"66","author":"BK Singh","year":"2016","unstructured":"Singh BK, Verma K, Thoke A (2016) Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Syst Appl 66:114\u2013123","journal-title":"Expert Syst Appl"},{"key":"8992_CR53","unstructured":"Society AC (2022) Breast cancer facts & figures 2021\u20132022"},{"key":"8992_CR54","doi-asserted-by":"crossref","unstructured":"Song JH, Venkatesh SS, MD EFC, Cary TW, MD PHA, Sehgal CM (2005) Artificial neural network to aid differentiation of malignant and benign breast masses by ultrasound imaging. In: Walker WF, Emelianov SY (eds) Medical Imaging 2005: Ultrasonic Imaging and Signal Processing, International Society for Optics and Photonics, SPIE, vol 5750, pp 148 \u2013 152","DOI":"10.1117\/12.595295"},{"key":"8992_CR55","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8992_CR56","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"364\u2013365","key":"8992_CR57","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ins.2016.05.021","volume":"100","author":"D Wang","year":"2016","unstructured":"Wang D (2016) Randomized algorithms for training neural networks. Inf Sci 100(364\u2013365):126\u2013128","journal-title":"Inf Sci"},{"key":"8992_CR58","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.cmpb.2018.05.028","volume":"162","author":"Z Wang","year":"2018","unstructured":"Wang Z, Xin J, Sun P, Lin Z, Yao Y, Gao X (2018) Improved lung nodule diagnosis accuracy using lung ct images with uncertain class. Comput Methods Programs Biomed 162:197\u2013209","journal-title":"Comput Methods Programs Biomed"},{"key":"8992_CR59","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1016\/j.neucom.2015.08.048","volume":"173","author":"W Xie","year":"2016","unstructured":"Xie W, Li Y, Ma Y (2016) Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173:930\u2013941","journal-title":"Neurocomputing"},{"issue":"12","key":"8992_CR60","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1109\/TMI.2013.2279938","volume":"32","author":"M Yang","year":"2013","unstructured":"Yang M, Moon WK, Wang YF, Bae MS, Huang C, Chen J, Chang R (2013) Robust texture analysis using multi-resolution gray-scale invariant features for breast sonographic tumor diagnosis. IEEE Trans Med Imaging 32(12):2262\u20132273","journal-title":"IEEE Trans Med Imaging"},{"issue":"17","key":"8992_CR61","doi-asserted-by":"crossref","first-page":"22,629","DOI":"10.1007\/s11042-017-5023-0","volume":"77","author":"YD Zhang","year":"2018","unstructured":"Zhang YD, Zhao G, Sun J, Wu X, Wang ZH, Liu HM, Govindaraj VV, Zhan T, Li J (2018) Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and jaya algorithm. Multimedia Tools and Applications 77(17):22,629-22,648","journal-title":"Multimedia Tools and Applications"},{"issue":"6","key":"8992_CR62","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.bspc.2013.06.011","volume":"8","author":"S Zhou","year":"2013","unstructured":"Zhou S, Shi J, Zhu J, Cai Y, Wang R (2013) Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image. Biomed Signal Process Control 8(6):688\u2013696","journal-title":"Biomed Signal Process Control"},{"issue":"106","key":"8992_CR63","first-page":"221","volume":"208","author":"Z Zhuang","year":"2021","unstructured":"Zhuang Z, Yang Z, Raj ANJ, Wei C, Jin P, Zhuang S (2021) Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Comput Methods Programs Biomed 208(106):221","journal-title":"Comput Methods Programs Biomed"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08992-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08992-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08992-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T01:29:15Z","timestamp":1743902955000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08992-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,20]]},"references-count":63,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["8992"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08992-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,20]]},"assertion":[{"value":"20 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}