{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:25:24Z","timestamp":1772252724733,"version":"3.50.1"},"reference-count":132,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Much work has recently identified the need to combine deep learning with extreme learning in order to strike a performance balance with accuracy, especially in the domain of multimedia applications. When considering this new paradigm\u2014namely, the convolutional extreme learning machine (CELM)\u2014we present a systematic review that investigates alternative deep learning architectures that use the extreme learning machine (ELM) for faster training to solve problems that are based on image analysis. We detail each of the architectures that are found in the literature along with their application scenarios, benchmark datasets, main results, and advantages, and then present the open challenges for CELM. We followed a well-structured methodology and established relevant research questions that guided our findings. Based on 81 primary studies, we found that object recognition is the most common problem that is solved by CELM, and CCN with predefined kernels is the most common CELM architecture proposed in the literature. The results from experiments show that CELM models present good precision, convergence, and computational performance, and they are able to decrease the total processing time that is required by the learning process. The results presented in this systematic review are expected to contribute to the research area of CELM, providing a good starting point for dealing with some of the current problems in the analysis of computer vision based on images.<\/jats:p>","DOI":"10.3390\/informatics8020033","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T03:28:36Z","timestamp":1620962916000},"page":"33","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Convolutional Extreme Learning Machines: A Systematic Review"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8242-9059","authenticated-orcid":false,"given":"Iago Richard","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco (UFPE), Recife 50670-420, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8109-697X","authenticated-orcid":false,"given":"Sebasti\u00e3o Rog\u00e9rio","family":"da Silva Neto","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia da Computa\u00e7\u00e3o, Universidade de Pernambuco (UPE), Recife 50050-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2673-5887","authenticated-orcid":false,"given":"Judith","family":"Kelner","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco (UFPE), Recife 50670-420, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5378-4732","authenticated-orcid":false,"given":"Djamel","family":"Sadok","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco (UFPE), Recife 50670-420, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9163-5583","authenticated-orcid":false,"given":"Patricia Takako","family":"Endo","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia da Computa\u00e7\u00e3o, Universidade de Pernambuco (UPE), Recife 50050-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MCI.2015.2405316","article-title":"Local Receptive Fields Based Extreme Learning Machine","volume":"10","author":"Huang","year":"2015","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cao, J., and Lin, Z. (2015). Extreme learning machines on high dimensional and large data applications: A survey. Math. Probl. Eng., 2015.","DOI":"10.1155\/2015\/103796"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/ACCESS.2015.2450498","article-title":"High-performance extreme learning machines: A complete toolbox for big data applications","volume":"3","author":"Akusok","year":"2015","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.neucom.2018.10.063","article-title":"Deep convolutional extreme learning machines: Filters combination and error model validation","volume":"329","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","article-title":"Extreme learning machines: A survey","volume":"2","author":"Huang","year":"2011","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","article-title":"Trends in extreme learning machines: A review","volume":"61","author":"Huang","year":"2015","journal-title":"Neural Networks"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.neucom.2017.06.037","article-title":"Extreme learning machine based transfer learning algorithms: A survey","volume":"267","author":"Salaken","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8925","DOI":"10.1016\/j.jfranklin.2020.04.033","article-title":"Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines","volume":"357","author":"Zhang","year":"2020","journal-title":"J. Frankl. Inst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Endo, P.T., Rodrigues, M., Gon\u00e7alves, G.E., Kelner, J., Sadok, D.H., and Curescu, C. (2016). High availability in clouds: Systematic review and research challenges. J. Cloud Comput., 5.","DOI":"10.1186\/s13677-016-0066-8"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s12243-014-0450-7","article-title":"Elasticity in cloud computing: A survey","volume":"70","author":"Coutinho","year":"2015","journal-title":"Ann. Telecommun.-Ann. T\u00e9l\u00e9commun."},{"key":"ref_17","first-page":"1","article-title":"Procedures for performing systematic reviews","volume":"33","author":"Kitchenham","year":"2004","journal-title":"Keele, UK Keele Univ."},{"key":"ref_18","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_21","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xu, X., Li, G., Xie, G., Ren, J., and Xie, X. (2019). Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions. Complexity, 2019.","DOI":"10.1155\/2019\/9180391"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yoo, Y., and Oh, S.Y. (2016, January 24\u201329). Fast training of convolutional neural network classifiers through extreme learning machines. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727403"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.procs.2015.07.316","article-title":"Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine","volume":"53","author":"Bai","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s11045-018-0598-9","article-title":"Local receptive fields based extreme learning machine with hybrid filter kernels for image classification","volume":"30","author":"He","year":"2019","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8157","DOI":"10.1007\/s00521-019-04303-9","article-title":"Extreme learning machine with autoencoding receptive fields for image classification","volume":"32","author":"Wu","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s11045-020-00708-1","article-title":"Extreme learning machine with multi-structure and auto encoding receptive fields for image classification","volume":"31","author":"Wu","year":"2020","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1007\/s10489-019-01584-4","article-title":"Two novel ELM-based stacking deep models focused on image recognition","volume":"50","author":"Song","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s11063-019-10079-9","article-title":"ELMAENet: A Simple, Effective and Fast Deep Architecture for Image Classification","volume":"51","author":"Chang","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alshalali, T., and Josyula, D. (2018, January 12\u201314). Fine-Tuning of Pre-Trained Deep Learning Models with Extreme Learning Machine. Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI46756.2018.00096"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Han, J.S., Cho, G.B., and Kwak, K.C. (2017, January 24\u201326). A Design of Convolutional Neural Network Using ReLU-Based ELM Classifier and Its Application. Proceedings of the 9th International Conference on Machine Learning and Computing, Singapore.","DOI":"10.1145\/3055635.3056609"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hao, P., Zhai, J.H., and Zhang, S.F. (2017, January 9\u201312). A simple and effective method for image classification. Proceedings of the 2017 International Conference on Machine Learning and Cybernetics (ICMLC), Ningbo, China.","DOI":"10.1109\/ICMLC.2017.8107769"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cui, D., Zhang, G., Han, W., Lekamalage Chamara Kasun, L., Hu, K., and Huang, G.B. (2017, January 22\u201329). Compact feature representation for image classification using elms. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.124"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"29271","DOI":"10.1007\/s11042-018-6781-z","article-title":"Deep convolutional representations and kernel extreme learning machines for image classification","volume":"78","author":"Zhu","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.neucom.2017.02.016","article-title":"Deep object recognition across domains based on adaptive extreme learning machine","volume":"239","author":"Zhang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_37","first-page":"249","article-title":"SVM and ELM: Who Wins? Object recognition with deep convolutional features from ImageNet","volume":"Volume 1","author":"Zhang","year":"2016","journal-title":"Proceedings of ELM-2015"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s12293-017-0229-2","article-title":"Active object recognition using hierarchical local-receptive-field-based extreme learning machine","volume":"10","author":"Liu","year":"2018","journal-title":"Memetic Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, X., Liu, H., and Huang, W. (2017, January 27\u201331). Room categorization using local receptive fields-based extreme learning machine. Proceedings of the 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), Hefei and Tai\u2019an, China.","DOI":"10.1109\/ICARM.2017.8273234"},{"key":"ref_40","unstructured":"LeCun, Y., Huang, F.J., and Bottou, L. (July, January 27). Learning methods for generic object recognition with invariance to pose and lighting. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, Washington, DC, USA."},{"key":"ref_41","unstructured":"Krizhevsky, A., and Hinton, G. (2009, April 08). Learning Multiple Layers of Features From Tiny Images. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.222.9220&rep=rep1&type=pdf."},{"key":"ref_42","unstructured":"Nene, S.A., Nayar, S.K., and Murase, H. (1996). Columbia Object Image Library (Coil-100), Columbia University."},{"key":"ref_43","unstructured":"Fei-Fei, L., Fergus, R., and Perona, P. (July, January 27). Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA."},{"key":"ref_44","unstructured":"Leibe, B., and Schiele, B. (2003, January 18\u201320). Analyzing appearance and contour based methods for object categorization. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108070","DOI":"10.1109\/ACCESS.2019.2932909","article-title":"Research on Optimization Methods of ELM Classification Algorithm for Hyperspectral Remote Sensing Images","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"132240","DOI":"10.1109\/ACCESS.2019.2940697","article-title":"A Spectral-Spatial Domain-Specific Convolutional Deep Extreme Learning Machine for Supervised Hyperspectral Image Classification","volume":"7","author":"Shen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shi, J., and Ku, J. (2017, January 10\u201312). Spectral-spatial classification of hyperspectral image using distributed extreme learning machine with MapReduce. Proceedings of the 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, China.","DOI":"10.1109\/ICBDA.2017.8078729"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cao, F., Yang, Z., Ren, J., and Ling, B.W.K. (2018). Convolutional neural network extreme learning machine for effective classification of hyperspectral images. J. Appl. Remote Sens., 12.","DOI":"10.1117\/1.JRS.12.035003"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/LGRS.2017.2786272","article-title":"Classification of hyperspectral imagery using a new fully convolutional neural network","volume":"15","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","first-page":"434","article-title":"Classification of hyperspectral remote sensing image using hierarchical local-receptive-field-based extreme learning machine","volume":"13","author":"Lv","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lv, Q., Niu, X., Dou, Y., Wang, Y., Xu, J., and Zhou, J. (2016, January 25\u201328). Hyperspectral image classification via kernel extreme learning machine using local receptive fields. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532358"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.3233\/JIFS-169031","article-title":"Leveraging local receptive fields based random weights networks for hyperspectral image classification","volume":"31","author":"Lv","year":"2016","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shen, Y., Chen, J., and Xiao, L. (2017, January 17\u201320). Supervised classification of hyperspectral images using local-receptive-fields-based kernel extreme learning machine. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296857"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gu, Y., Xu, Y., and Liu, J. (2019, January 2\u20135). SAR ATR by Decision Fusion of Multiple Random Convolution Features. Proceedings of the 2019 22th International Conference on Information Fusion (FUSION), Ottawa, ON, Canada.","DOI":"10.23919\/FUSION43075.2019.9011249"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, X., and Hao, Y. (2019). A Method Combining CNN and ELM for Feature Extraction and Classification of SAR Image. J. Sens., 2019.","DOI":"10.1155\/2019\/6134610"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1080\/01431161.2018.1533655","article-title":"Aerial scene classification via an ensemble extreme learning machine classifier based on discriminative hybrid convolutional neural networks features","volume":"40","author":"Ye","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","unstructured":"Romay, D.M.G. (2020). Hyperspectral Remote Sensing Scenes, Universidad del Pa\u00eds Vasco (UPV\/EHU)."},{"key":"ref_59","first-page":"228","article-title":"MSTAR extended operating conditions: A tutorial","volume":"Volume 2757","author":"Keydel","year":"1996","journal-title":"Algorithms for Synthetic Aperture Radar Imagery III"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Coman, C. (2018, January 20\u201322). A deep learning sar target classification experiment on mstar dataset. Proceedings of the 2018 19th International Radar Symposium (IRS), Bonn, Germany.","DOI":"10.23919\/IRS.2018.8448048"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"\u00d6zyurt, F., Sert, E., and Avc\u0131, D. (2020). An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine. Med Hypotheses, 134.","DOI":"10.1016\/j.mehy.2019.109433"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Pashaei, A., Sajedi, H., and Jazayeri, N. (2018, January 25\u201326). Brain tumor classification via convolutional neural network and extreme learning machines. Proceedings of the 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE.2018.8566571"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.3906\/elk-1801-8","article-title":"Deep learning based brain tumor classification and detection system","volume":"26","author":"Ari","year":"2018","journal-title":"TUrkish J. Electr. Eng. Comput. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Yu, J.S., Chen, J., Xiang, Z., and Zou, Y.X. (2015, January 6\u20139). A hybrid convolutional neural networks with extreme learning machine for WCE image classification. Proceedings of the 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, China.","DOI":"10.1109\/ROBIO.2015.7419037"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.measurement.2019.01.060","article-title":"A novel approach for liver image classification: PH-C-ELM","volume":"137","year":"2019","journal-title":"Measurement"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"8163","DOI":"10.1007\/s00500-019-04383-8","article-title":"A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine","volume":"24","year":"2020","journal-title":"Soft Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1007\/s13042-018-0825-6","article-title":"Local receptive field based extreme learning machine with three channels for histopathological image classification","volume":"10","author":"Fang","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lu, S., Xia, K., and Wang, S.H. (2020). Diagnosis of cerebral microbleed via VGG and extreme learning machine trained by Gaussian map bat algorithm. J. Ambient. Intell. Humaniz. Comput., 1\u201312.","DOI":"10.1007\/s12652-020-01789-3"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.future.2019.09.015","article-title":"Cervical cancer classification using convolutional neural networks and extreme learning machines","volume":"102","author":"Ghoneim","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"5564","DOI":"10.1109\/ACCESS.2018.2889350","article-title":"Ensemble learning of multiple-view 3D-CNNs model for micro-nodules identification in CT images","volume":"7","author":"Monkam","year":"2018","journal-title":"IEEE Access"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Fang, L., Wang, C., Li, S., Yan, J., Chen, X., and Rabbani, H. (2017). Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels. J. Biomed. Opt., 22.","DOI":"10.1117\/1.JBO.22.11.116011"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.compbiomed.2017.03.017","article-title":"Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading","volume":"84","author":"Li","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Baldominos, A., Saez, Y., and Isasi, P. (2019). A survey of handwritten character recognition with mnist and emnist. Appl. Sci., 9.","DOI":"10.3390\/app9153169"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Khellal, A., Ma, H., and Fei, Q. (2018, January 25\u201327). Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8482876"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Kannojia, S.P., and Jaiswal, G. (2018, January 22\u201323). Ensemble of hybrid CNN-ELM model for image classification. Proceedings of the 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2018.8474196"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1007\/s00521-015-2170-y","article-title":"Extreme learning machine with kernel model based on deep learning","volume":"28","author":"Ding","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Pang, S., and Yang, X. (2016). Deep convolutional extreme learning machine and its application in handwritten digit classification. Comput. Intell. Neurosci., 2016.","DOI":"10.1155\/2016\/3049632"},{"key":"ref_78","unstructured":"LeCun, Y., Cortes, C., and Burges, C. (2020, August 25). THE MNIST DATABASE: Of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/34.291440","article-title":"A database for handwritten text recognition research","volume":"16","author":"Hull","year":"1994","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S.Z., and Hospedales, T. (2015, January 7\u201313). When face recognition meets with deep learning: An evaluation of convolutional neural networks for face recognition. Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.58"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"12919","DOI":"10.1007\/s11042-017-4923-3","article-title":"2DPCANet: A deep leaning network for face recognition","volume":"77","author":"Yu","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_82","unstructured":"Ripon, K.S.N., Ali, L.E., Siddique, N., and Ma, J. (2019, January 14\u201319). Convolutional Neural Network based Eye Recognition from Distantly Acquired Face Images for Human Identification. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Wang, K., Liu, M., Hao, X., and Xing, X. (2017). Decision-Level Fusion Method Based on Deep Learning. Proceedings of the Chinese Conference on Biometric Recognition, Shenzhen, China, 28\u201329 October 2017, Springer.","DOI":"10.1007\/978-3-319-69923-3_72"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"G\u00fcrp\u0131nar, F., Kaya, H., and Salah, A.A. (2016). Combining deep facial and ambient features for first impression estimation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-49409-8_30"},{"key":"ref_85","unstructured":"Yale (2020, August 25). The Normalized Yale Face Database. Available online: https:\/\/vismod.media.mit.edu\/vismod\/classes\/mas622-00\/datasets\/."},{"key":"ref_86","first-page":"1457","article-title":"Non-negative matrix factorization with sparseness constraints","volume":"5","author":"Hoyer","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"4313","DOI":"10.1007\/s11042-016-3374-6","article-title":"RGB-D datasets using microsoft kinect or similar sensors: A survey","volume":"76","author":"Cai","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.cviu.2018.04.007","article-title":"RGB-D-based human motion recognition with deep learning: A survey","volume":"171","author":"Wang","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Shao, L., Han, J., Kohli, P., and Zhang, Z. (2014). Computer Vision and Machine Learning with RGB-D Sensors, Springer International Publishing.","DOI":"10.1007\/978-3-319-08651-4"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Boubou, S., Narikiyo, T., and Kawanishi, M. (2017, January 3\u20137). Object recognition from 3d depth data with extreme learning machine and local receptive field. Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Munich, Germany.","DOI":"10.1109\/AIM.2017.8014049"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.neucom.2017.04.077","article-title":"Multi-modal local receptive field extreme learning machine for object recognition","volume":"277","author":"Liu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"8181","DOI":"10.1007\/s10586-018-1695-0","article-title":"Multi-view CSPMPR-ELM feature learning and classifying for RGB-D object recognition","volume":"22","author":"Yin","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1007\/s12652-018-1067-x","article-title":"RGB-D object recognition based on the joint deep random kernel convolution and ELM","volume":"11","author":"Yin","year":"2018","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s10514-018-9776-8","article-title":"Viewpoint invariant semantic object and scene categorization with RGB-D sensors","volume":"43","author":"Zaki","year":"2019","journal-title":"Auton. Robot."},{"key":"ref_95","first-page":"106051Z","article-title":"Multi-model convolutional extreme learning machine with kernel for RGB-D object recognition","volume":"Volume 10605","author":"Yin","year":"2017","journal-title":"LIDAR Imaging Detection and Target Recognition 2017"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1007\/s12559-018-9598-1","article-title":"Multi-view cnn feature aggregation with elm auto-encoder for 3d shape recognition","volume":"10","author":"Yang","year":"2018","journal-title":"Cogn. Comput."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.patcog.2017.07.013","article-title":"Human action recognition in RGB-D videos using motion sequence information and deep learning","volume":"72","author":"Ijjina","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Lai, K., Bo, L., Ren, X., and Fox, D. (2011, January 9\u201313). A large-scale hierarchical multi-view rgb-d object dataset. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980382"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Martinel, N., Piciarelli, C., Foresti, G.L., and Micheloni, C. (2016, January 12\u201315). Mobile food recognition with an extreme deep tree. Proceedings of the 10th International Conference on Distributed Smart Camera, Paris, France.","DOI":"10.1145\/2967413.2967428"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhu, X., Wang, L., and Guo, P. (2018, January 7\u201310). Image classification using convolutional neural networks and kernel extreme learning machines. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451560"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Horii, K., Maeda, K., Ogawa, T., and Haseyama, M. (2018, January 7\u201310). A Human-Centered Neural Network Model with Discriminative Locality Preserving Canonical Correlation Analysis for Image Classification. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451293"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s11554-019-00852-3","article-title":"Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images","volume":"17","author":"Pashaei","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Xu, X., Fang, Y., and Zhao, K. (2015, January 22). Traffic sign recognition using deep convolutional networks and extreme learning machine. Proceedings of the International Conference on Intelligent Science and Big Data Engineering, Cham, Switzerland.","DOI":"10.1007\/978-3-319-23989-7_28"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Liu, Q., Zhao, Y., and Li, W. (2018, January 16\u201318). Aluminum Foil Packaging Sealing Testing Method Based on Gabor Wavelet and ELM Neural Network. Proceedings of the 2nd International Conference on Advances in Image Processing, Chengdu, China.","DOI":"10.1145\/3239576.3239583"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s12559-018-9571-z","article-title":"Surface material recognition using active multi-modal extreme learning machine","volume":"10","author":"Liu","year":"2018","journal-title":"Cogn. Comput."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Xu, X., Fang, J., Li, Q., Xie, G., Xie, J., and Ren, M. (2018, January 28). Multi-scale local receptive field based online sequential extreme learning machine for material classification. Proceedings of the International Conference on Cognitive Systems and Signal Processing, Singapore.","DOI":"10.1007\/978-981-13-7983-3_4"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.neucom.2015.03.117","article-title":"A novel biologically inspired ELM-based network for image recognition","volume":"174","author":"Zhang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1007\/s00371-019-01725-3","article-title":"Deep motion templates and extreme learning machine for sign language recognition","volume":"36","author":"Imran","year":"2020","journal-title":"Vis. Comput."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Xie, X., Guo, W., and Jiang, T. (2018, January 14). Body Gestures Recognition Based on CNN-ELM Using Wi-Fi Long Preamble. Proceedings of the International Conference in Communications, Signal Processing, and Systems, Singapore.","DOI":"10.1007\/978-981-13-6504-1_106"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"7543","DOI":"10.1007\/s11042-018-6491-6","article-title":"Robust visual tracking based on convolutional neural network with extreme learning machine","volume":"78","author":"Sun","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1007\/s11045-016-0414-3","article-title":"Extreme learning machine with multi-scale local receptive fields for texture classification","volume":"28","author":"Huang","year":"2017","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"K\u00f6lsch, A., Afzal, M.Z., Ebbecke, M., and Liwicki, M. (2017, January 9\u201315). Real-time document image classification using deep CNN and extreme learning machines. Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.217"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Li, D., Qiu, X., Zhu, Z., and Liu, Y. (2018, January 25\u201326). Criminal Investigation Image Classification Based on Spatial CNN Features and ELM. Proceedings of the 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC.2018.10173"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s13042-017-0736-y","article-title":"Haptic recognition using hierarchical extreme learning machine with local-receptive-field","volume":"10","author":"Li","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Sharma, J., Granmo, O.C., and Goodwin, M. (2018, January 27). Deep CNN-ELM Hybrid Models for Fire Detection in Images. Proceedings of the International Conference on Artificial Neural Networks, Cham, Switzerland.","DOI":"10.1007\/978-3-030-01424-7_25"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"36283","DOI":"10.1109\/ACCESS.2018.2848966","article-title":"Multiple features with extreme learning machines for clothing image recognition","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1049\/iet-its.2017.0136","article-title":"Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features","volume":"12","author":"Yang","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/34.667881","article-title":"On combining classifiers","volume":"20","author":"Kittler","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","article-title":"PCANet: A simple deep learning baseline for image classification?","volume":"24","author":"Chan","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.patcog.2017.07.019","article-title":"On automated source selection for transfer learning in convolutional neural networks","volume":"73","author":"Afridi","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.eswa.2017.11.028","article-title":"A new image classification method using CNN transfer learning and web data augmentation","volume":"95","author":"Han","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"4395","DOI":"10.1007\/s11042-019-7222-3","article-title":"Human-centered image classification via a neural network considering visual and biological features","volume":"79","author":"Horii","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_123","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 21\u201325). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_127","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv."},{"key":"ref_128","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.neucom.2018.12.080","article-title":"Convolutional neural network based on an extreme learning machine for image classification","volume":"339","author":"Park","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Khellal, A., Ma, H., and Fei, Q. (2018). Convolutional neural network based on extreme learning machine for maritime ships recognition in infrared images. Sensors, 18.","DOI":"10.3390\/s18051490"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neunet.2016.12.002","article-title":"Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection","volume":"87","author":"Kim","year":"2017","journal-title":"Neural Netw."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/2\/33\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:00:11Z","timestamp":1760162411000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/2\/33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,13]]},"references-count":132,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["informatics8020033"],"URL":"https:\/\/doi.org\/10.3390\/informatics8020033","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202104.0753.v1","asserted-by":"object"}]},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,13]]}}}