Extreme Learning Machine for Biomedical Image Classification: A Multi-Case Study

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5542

Keywords:

Extreme Learning Machine, ELM, Biomedical Image, Classification, Machine Learning

Abstract

In the current realm of biomedical image classification, the predominant choice remains deep learning networks, particularly convolutional neural network (CNN) models. However, deep learning suffers from a notable drawback in terms of its high training cost, mainly due to intricate data models. A recent alternative, known as the Extreme Learning Machine (ELM), has emerged as a promising solution. Empirical investigations have indicated that ELM can offer satisfactory predictive performance for a wide array of classification tasks, while significantly reducing training costs when compared to deep learning networks trained using back propagation.
This research paper introduces a methodology designed to evaluate the suitability of employing the Extreme Learning Machine for biomedical classification tasks. Our study encompasses binary and multiclass classification across four distinct scenarios, involving the analysis of biomedical images obtained from both dermatoscopes and blood cell microscopes. The findings underscore the effectiveness of the Extreme Learning Machine, showcasing its successful utilization in the classification of biomedical images.

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References

RUMELHART, D.E., HINTON, G.E. and WILLIAMS, R.J. (1986) Learning representations by back-propagating errors. nature 323(6088): 533–536. DOI: https://doi.org/10.1038/323533a0

HAGAN, M.T. and MENHAJ, M.B. (1994) Training feedforward networks with the marquardt algorithm. IEEE transactions on Neural Networks 5(6): 989–993. DOI: https://doi.org/10.1109/72.329697

CHEN, S., COWAN, C. and GRANT, P. (1991) Orthogonal least squares learning algorithm for radial. IEEE Transactions on neural networks 2(2): 303. DOI: https://doi.org/10.1109/72.80341

URGEN BRANKE, J. (1995) Evolutionary algorithms for neural network design and training. In Proceedings of the 1st Nordic Workshop on Genetic Algorithms and its Applictions (Citeseer).

HUANG, G., HUANG, G.B., SONG, S. and YOU, K. (2015) Trends in extreme learning machines: A review. Neural Networks 61: 32–48. DOI: https://doi.org/10.1016/j.neunet.2014.10.001

HUANG, G.B. and CHEN, L. (2007) Convex incremental extreme learning machine. Neurocomputing 70(16-18): 3056–3062. DOI: https://doi.org/10.1016/j.neucom.2007.02.009

HUANG, G.B., CHEN, L., SIEW, C.K. et al. (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Networks 17(4): 879–892. DOI: https://doi.org/10.1109/TNN.2006.875977

LIU, X., LIN, S., FANG, J. and XU, Z. (2014) Is extreme learning machine feasible? a theoretical assessment (part i). IEEE Transactions on Neural Networks and Learning Systems 26(1): 7–20. DOI: https://doi.org/10.1109/TNNLS.2014.2335212

LIN, S., LIU, X., FANG, J. and XU, Z. (2014) Is extreme learning machine feasible? a theoretical assessment (part ii). IEEE Transactions on Neural Networks and Learning Systems 26(1): 21–34. DOI: https://doi.org/10.1109/TNNLS.2014.2336665

HUANG, G.B., ZHOU, H., DING, X. and ZHANG, R. (2011) Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42(2): 513–529. DOI: https://doi.org/10.1109/TSMCB.2011.2168604

CORTES, C. and VAPNIK, V. (1995) Support vector machine. Machine learning 20(3): 273–297. DOI: https://doi.org/10.1007/BF00994018

SUYKENS, J.A. and VANDEWALLE, J. (1999) Least squares support vector machine classifiers. Neural processing letters 9(3): 293–300. DOI: https://doi.org/10.1023/A:1018628609742

FERNÁNDEZ-DELGADO, M., CERNADAS, E., BARRO, S., RIBEIRO, J. and NEVES, J. (2014) Direct kernel perceptron (dkp): Ultra-fast kernel elm-based classification with noniterative closed-form weight calculation. Neural Networks 50: 60–71. DOI: https://doi.org/10.1016/j.neunet.2013.11.002

HUANG, G., SONG, S., GUPTA, J.N. and WU, C. (2014) Semi-supervised and unsupervised extreme learning machines. IEEE transactions on cybernetics 44(12): 2405–2417. DOI: https://doi.org/10.1109/TCYB.2014.2307349

HUANG, G.B. (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation 6(3): 376–390. DOI: https://doi.org/10.1007/s12559-014-9255-2

HUANG, G., SONG, S. and WU, C. (2012) Orthogonal least squares algorithm for training cascade neural networks. IEEE Transactions on Circuits and Systems I: Regular Papers 59(11): 2629–2637. DOI: https://doi.org/10.1109/TCSI.2012.2189060

REDDY, M.B.S. and RANA, P. (2021) Biomedical image classification using deep convolutional neural networks–overview. In IOP Conference Series: Materials Science and Engineering (IOP Publishing), 1022: 012020. DOI: https://doi.org/10.1088/1757-899X/1022/1/012020

HUANG, P., HE, P., TIAN, S., MA, M., FENG, P., XIAO, H., MERCALDO, F. et al. (2022) A vit-amc network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images. IEEE Transactions on Medical Imaging 42(1): 15–28. DOI: https://doi.org/10.1109/TMI.2022.3202248

HUANG, P., TAN, X., ZHOU, X., LIU, S., MERCALDO, F. and SANTONE, A. (2021) Fabnet: fusion attention block and transfer learning for laryngeal cancer tumor grading in p63 ihc histopathology images. IEEE Journal of Biomedical and Health Informatics 26(4): 1696–1707. DOI: https://doi.org/10.1109/JBHI.2021.3108999

HUANG, P., ZHOU, X., HE, P., FENG, P., TIAN, S., SUN, Y., MERCALDO, F. et al. (2023) Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient cam and priori experienceguided attention. Computers in Biology and Medicine 154: 106447. DOI: https://doi.org/10.1016/j.compbiomed.2022.106447

CIMITILE, A., MARTINELLI, F. and MERCALDO, F. (2017) Machine learning meets ios malware: Identifying malicious applications on apple environment. In ICISSP: 487–492. DOI: https://doi.org/10.5220/0006217304870492

BACCI, A., BARTOLI, A., MARTINELLI, F., MEDVET, E. and MERCALDO, F. (2018) Detection of obfuscation techniques in android applications. In Proceedings of the 13th International Conference on Availability, Reliability and Security: 1–9. DOI: https://doi.org/10.1145/3230833.3232823

CASOLARE, R., MARTINELLI, F., MERCALDO, F. and SANTONE, A. (2019) A model checking based proposal for mobile colluding attack detection. In 2019 IEEE International Conference on Big Data (Big Data) (IEEE): 5998–6000. DOI: https://doi.org/10.1109/BigData47090.2019.9006094

DEEPA, S., DEVI, B.A. et al. (2011) A survey on artificial intelligence approaches for medical image classification. Indian Journal of Science and Technology 4(11): 1583–1595. DOI: https://doi.org/10.17485/ijst/2011/v4i11.35

ALI, M., SARWAR, A., SHARMA, V. and SURI, J. (2019) Artificial neural network based screening of cervical cancer using a hierarchical modular neural network architecture (hmnna) and novel benchmark uterine cervix cancer database. Neural Computing and Applications 31(7): 2979–2993. DOI: https://doi.org/10.1007/s00521-017-3246-7

URUSHIBARA, A., SAIDA, T., MORI, K., ISHIGURO, T., SAKAI, M., MASUOKA, S., SATOH, T. et al. (2021) Diagnosing uterine cervical cancer on a single t2-weighted image: comparison between deep learning versus radiologists. European Journal of Radiology 135: 109471. DOI: https://doi.org/10.1016/j.ejrad.2020.109471

MOHSEN, H., EL-DAHSHAN, E.S.A., EL-HORBATY, E.S.M. and SALEM, A.B.M. (2018) Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal 3(1): 68–71. DOI: https://doi.org/10.1016/j.fcij.2017.12.001

ZIA, R., AKHTAR, P. and AZIZ, A. (2018) A new rectangular window based image cropping method for generalization of brain neoplasm classification systems. International Journal of Imaging Systems and Technology 28(3): 153–162. DOI: https://doi.org/10.1002/ima.22266

JAIN, M., ANDREOPOULOS, W. and STAMP, M. (2020) Convolutional neural networks and extreme learning machines for malware classification. Journal of Computer Virology and Hacking Techniques 16(3): 229–244. DOI: https://doi.org/10.1007/s11416-020-00354-y

KRIZHEVSKY, A., SUTSKEVER, I. and HINTON, G.E. (2017) Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6): 84–90. DOI: https://doi.org/10.1145/3065386

LU, S., LU, Z. and ZHANG, Y.D. (2019) Pathological brain detection based on alexnet and transfer learning. Journal of computational science 30: 41–47. DOI: https://doi.org/10.1016/j.jocs.2018.11.008

DHAR, P., DUTTA, S. and MUKHERJEE, V. (2021) Cross-wavelet assisted convolution neural network (alexnet) approach for phonocardiogram signals classification. Biomedical Signal Processing and Control 63: 102142. DOI: https://doi.org/10.1016/j.bspc.2020.102142

KUMAR M, A. and CHAKRAPANI, A. (2022) Classification of ecg signal using fft based improved alexnet classifier. Plos one 17(9): e0274225. DOI: https://doi.org/10.1371/journal.pone.0274225

SINGH, P., MANURE, A., SINGH, P. and MANURE, A. (2020) Introduction to tensorflow 2.0. Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python : 1–24. DOI: https://doi.org/10.1007/978-1-4842-5558-2_1

YANG, J., SHI, R. and NI, B. (2021) Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (IEEE): 191–195. DOI: https://doi.org/10.1109/ISBI48211.2021.9434062

TSCHANDL, P., ROSENDAHL, C. and KITTLER, H. (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5(1): 1–9. DOI: https://doi.org/10.1038/sdata.2018.161

CODELLA, N., ROTEMBERG, V., TSCHANDL, P., CELEBI, M.E., DUSZA, S., GUTMAN, D., HELBA, B. et al. (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902.03368 .

ACEVEDO, A., MERINO, A., ALFÉREZ, S., MOLINA, Á., BOLDÚ, L. and RODELLAR, J. (2020) A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data in brief 30. DOI: https://doi.org/10.1016/j.dib.2020.105474

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Published

25-03-2024

How to Cite

1.
Mercaldo F, Brunese L, Santone A, Martinelli F, Cesarelli M. Extreme Learning Machine for Biomedical Image Classification: A Multi-Case Study. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 25 [cited 2024 Apr. 21];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5542