Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images

Nayak, Deepak Ranjan; Das, Dibyasundar; Dash, Ratnakar; Majhi, Snehashis; Majhi, Banshidhar

doi:10.1007/s11042-019-7233-0

Cited by 36 publications

(19 citation statements)

References 35 publications

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“…For the last step, viz., the NLAF β , it usually selects the rectified linear unit (ReLU) function [33] . Suppose f ij is the entry of the matrix F , we have

…”

Section: Methodsmentioning

confidence: 99%

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

et al. 2021

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Highlights We analysed over 320 COVID-19 images and 320 healthy control images. We proposed an improved CNN to extract individual image-level features. We proposed to use GCN to extract relation-aware representations. We proposed a DFF technology to combine features from GCN and CNN. The proposed FCGNet gives better performance than 15 state-of-the-art approaches.

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“…For the last step, viz., the NLAF β , it usually selects the rectified linear unit (ReLU) function [33] . Suppose f ij is the entry of the matrix F , we have

…”

Section: Methodsmentioning

confidence: 99%

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

et al. 2021

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“…They also developed a new deep network based on GELM-AE which converges faster than conventional deep networks. Nayak and Das [119]used ELM-AE to form a multilayer ELM and employed leaky rectified linear unit (LReLU) as activation function, which can be written as…”

Section: Elm For Semi-supervised and Unsupervised Learningmentioning

confidence: 99%

“…So, concluding the restriction condition, the mathematical expression of SVM's goal is written as below [119] Leaky rectified linear unit was used in multilayer ELM.…”

Section: Elm Vs Support Vector Machine (Svm)mentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

279

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…First, CNN is able to improve capability and consistency in utilising picture functionality using deep architecture. Second, there has been a great deal of improvement in regularisation, including standardisation of the batch [45], LReLU [46] and residual learning [47]. These approaches allow CNN to accelerate and optimise the training of denoising networkefficiently.…”

Section: Denoising Networkmentioning

confidence: 99%

Image Denoising to enhance Character Recognition using Deep Learning

ruata

Hussain

2021

Preprint

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In this paper, we proposed implementing a Deep Convolutional Neural Network. A relationship between a noisy character image to its clean counter-part are mapped using Deep Convolutional Neural Network.The overall process is divided into two stages: noise type classification and image denoising. Firstly, the noise type classification identifies the types of noise, and based on this noise type, a particular denoising model is selected, which increases the image denoising performance. The denoising network inputs a noisy image and a target of its clean corresponding image during the training. After the mapping function is trained, the generated model performs character image denoising. Then, on each band, a trained mapping function perform image denoising irrespective of the other band. Finally, each block is assembled to generate a clean image. In this paper, the MNIST and Char74K dataset of handwritten digits diluted with artificial noise divided into ten types are used for experimentation.. Our experimental results show that the proposed techniques perform better image denoising ascompared to the existing methods, both in terms of image noise type classification and image denoising. The overall Character recognition accuracy increased by 66% after performing the proposed denoising technique.

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Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images

Cited by 36 publications

References 35 publications

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

A review on extreme learning machine

Image Denoising to enhance Character Recognition using Deep Learning

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