Local receptive field based extreme learning machine with three channels for histopathological image classification

Fang, Jing; Xu, Xin; Liu, Huaping; Sun, Fuchun

doi:10.1007/s13042-018-0825-6

Cited by 9 publications

(11 citation statements)

References 40 publications

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“…Dataset [64] Classification of digestive organs disease Own dataset [65] Liver tumor classification Elazig University Hospital [66] White blood cell detection BCCD dataset [67] Histopathological image classification ADL dataset [68] Cerebral microbleed diagnosis Own dataset [69] Cervical cancer classification Herlev dataset [61] Brain tumor classification CGA-GBM database [70] Micro-nodules classification LIDC/IDRI dataset [62] Brain tumor classification Brain T1-weighed CE-MRI dataset [63] Brain tumor classification Brain tumor MRI dataset [71] Classification of anomalies in the human retina Duke and HUCM datasets [72] Hepatocellular carcinoma classification ICPR 2014 HEp-2 cell dataset [73]. Several works proposed digit and character recognition for applications such as handwriting recognition [73].…”

Section: References Approachmentioning

confidence: 99%

“…The multilayer ELM-LRF is another known ELM-LRF variation which consists of multiple convolution and pooling layers [67], [27], [38], [51], [114], and [77].…”

Section: Random Filtermentioning

confidence: 99%

“…Also, some works consider all channels or variate scales (multiscale) of the images by applying different ELM-LRF architectures for feature extraction, and learning task [67], [111], [106], and [54].…”

Section: Random Filtermentioning

confidence: 99%

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Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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Section: References Approachmentioning

confidence: 99%

“…The multilayer ELM-LRF is another known ELM-LRF variation which consists of multiple convolution and pooling layers [67], [27], [38], [51], [114], and [77].…”

Section: Random Filtermentioning

confidence: 99%

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Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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“…In recent years, ELM has been used in various applications such as signal processing [18], image processing [19], [20], medical diagnosis [21], market analysis [22], aviation and aerospace [23], forecasting [24] and others [25]. In signal processing, [18] has applied the ELM algorithm to identify two different EEG (electroencephalogram) signals.…”

Section: Related Workmentioning

confidence: 99%

“…Experimental validation on the Washington RGB-D data set illustrates that the proposed combination method achieves better recognition performance. Whereas [19] proposed ELM based on local recruitment areas with three channels called 3C-LRF-ELM. This suggestion algorithm allows the hepatologic features to be automatically diagnose illness using a set of lungs, kidney and spleen image data sets.…”

Section: Related Workmentioning

confidence: 99%

Regularization Activation Function for Extreme Learning Machine

Ismail¹,

Othman²,

Samsudin³

2019

ijacsa

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Extreme Learning Machine (ELM) algorithm based on single hidden layer feedforward neural networks has shown as the best time series prediction technique. Furthermore, the algorithm has a good generalization performance with extremely fast learning speed. However, ELM facing overfitting problem that can affect the model quality due to the implementation using empirical risk minimization scheme. Therefore, this study aims to improve ELM by introducing an Activation Functions Regularization in ELM called RAF-ELM. The experiment has been conducted in two phases. First, investigating the modified RAF-ELM performance using four types of activation functions are: Sigmoid, Sine, Tribas and Hardlim. In this study, input weight and bias for hidden layers are randomly selected, whereas the best neurons number of hidden layer is determined from 5 to 100. This experiment used UCI benchmark datasets. The number of neurons (99) using Sigmoid activation function shown the best performance. The proposed methods has improved the accuracy performance and learning speed up to 0.016205 MAE and processing time 0.007 seconds respectively compared with conventional ELM and has improved up to 0.0354 MSE for accuracy performance compare with state of the art algorithm. The second experiment is to validate the proposed RAF-ELM using 15 regression benchmark dataset. RAF-ELM has been compared with four neural network techniques namely conventional ELM, Back Propagation, Radial Basis Function and Elman. The results show that RAF-ELM technique obtain the best performance compared to other techniques in term of accuracy for various time series data that come from various domain.

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Multi-scale Local Receptive Field Based Online Sequential Extreme Learning Machine for Material Classification

Fang

et al. 2019

Communications in Computer and Information Science

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Local receptive field based extreme learning machine with three channels for histopathological image classification

Cited by 9 publications

References 40 publications

Convolutional Extreme Learning Machines: A Systematic Review

Convolutional Extreme Learning Machines: A Systematic Review

Regularization Activation Function for Extreme Learning Machine

Multi-scale Local Receptive Field Based Online Sequential Extreme Learning Machine for Material Classification

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