Multi-path learnable wavelet neural network for image classification

Silva, D. D. N. De; Vithanage, H.W.M.K.; Fernando, K. S. D.; Piyatilake, I. T. S.

doi:10.1117/12.2556535

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…(4) Calculating the errors of back propagating for each layer, which are the results of derivation by the chain rule. (5) Applying gradient to adjust the weights and bias according to the back-propagated errors. (6) Repeating the step (2) to step (5) until the MSE is small enough.…”

Section: Algorithm Of Cnnmentioning

confidence: 99%

“…Wavelet transform (WT) is often used in deep learning [5,16,24]. Many features can be obtained by the discrete wavelet transform which have been improved by researches.…”

Section: Introductionmentioning

confidence: 99%

“…The applications based on wavelet neural network (WNN) in deep learning are as follows In 2019, Pengju Liu et al proposed a Multi-level Wavelet Convolutional Neural Networks(MWCNN) [16], which is proved to increase the receptive field by reducing the number of map. The Multi-Path Learnable Wavelet Neural Network for Image Classification was introduced by De Silva et al [5]. This model introduces a multi-path layout with several levels of wavelet decompositions.…”

Section: Introductionmentioning

confidence: 99%

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Research on improved wavelet convolutional wavelet neural networks

et al. 2020

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Convolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.

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Section: Algorithm Of Cnnmentioning

confidence: 99%

“…Wavelet transform (WT) is often used in deep learning [5,16,24]. Many features can be obtained by the discrete wavelet transform which have been improved by researches.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on improved wavelet convolutional wavelet neural networks

et al. 2020

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“…In image processing, wavelet transform is often used as a tool for content information analysis [34]. With the development of DNNs, wavelet transform has several attempts to combine the classical signal processing and deep learning methods, such as image denoising [20,31,47], super resolution [16,30], classification [7,25,29], segmentation [24], facial aging [32], style transfer [50], remote sensing image processing [9], etc. It is often used as the tool of data preprocessing, post-processing, feature extraction, and sampling operators in DNNs [16,32,39,48,30,23].…”

Section: Waveletsmentioning

confidence: 99%

MWQ: Multiscale Wavelet Quantized Neural Networks

Sun¹,

Ren²,

Jiao³

et al. 2021

Preprint

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Model quantization can reduce the model size and computational latency, it has become an essential technique for the deployment of deep neural networks on resourceconstrained hardware (e.g., mobile phones and embedded devices). The existing quantization methods mainly consider the numerical elements of the weights and activation values, ignoring the relationship between elements. The decline of representation ability and information loss usually lead to the performance degradation. Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method. This method decomposes original data into multiscale frequency components by wavelet transform, and then quantizes the components of different scales, respectively. It exploits the multiscale frequency and spatial information to alleviate the information loss caused by quantization in the spatial domain. Because of the flexibility of MWQ, we demonstrate three applications (e.g., model compression, quantized network optimization, and information enhancement) on the ImageNet and COCO datasets. Experimental results show that our method has stronger representation ability and can play an effective role in quantized neural networks.

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“…The authors use shallow networks to find the best wavelet in the wavelet parameter space in these early works. This approach has now been used with a deeper network for image classification, but the network is troublesome to train due to the high computational cost [89].…”

Section: Wavelet Theorymentioning

confidence: 99%

COVID-19 Diagnosis from Chest X-Ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super Resolution Convolutional Neural Network: Algorithm Development and Validation (Preprint)

Monday¹,

Li²,

Nneji³

et al. 2021

Preprint

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BACKGROUND The chest x-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease-19 (COVID-19). Despite the global COVID-19 uprising, utilizing computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce clinician burden. There is no dispute that low resolution, noisy and irrelevant annotations in chest x-ray images is a major constraint to the performance of AI-based COVID-19 diagnosis. While few studies have made huge progress, they underestimate these bottlenecks. OBJECTIVE In this study, we propose a Super Resolution based Siamese Wavelet Multi-Resolution Convolutional Neural Network called COVID-SRWCNN for COVID-19 Classification using chest x-ray images. METHODS Concretely, we first reconstruct high-resolution (HR) counterparts from low resolution (LR) images of CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest x-ray image. Since the datasets are collected from different sources with varying resolutions and the input layer of a convolutional neural network requires that the input size of the images in the training distribution must be fixed, therefore we extend the super resolution convolutional neural network by introducing an adaptive scaling operation to resize the images to a fixed resolution prior to the enhancement operation. Exploiting a mutual learning approach, the HR images are passed to the proposed siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. RESULTS We validate the proposed COVID-SRWCNN model on public-source datasets achieving an accuracy of 99.6%, precision of 99.7%, and F1 score of 99.9%. Our screening technique achieved 99.8 % AUC, 99.7% sensitivity and 99.6% specificity. CONCLUSIONS Owing to the fact that COVID-19 chest x-ray dataset are low in quality, experimental results show that our proposed algorithm obtained up-to-date performance which is useful for COVID-19 screening.

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Multi-path learnable wavelet neural network for image classification

Cited by 8 publications

References 34 publications

Research on improved wavelet convolutional wavelet neural networks

Research on improved wavelet convolutional wavelet neural networks

MWQ: Multiscale Wavelet Quantized Neural Networks

COVID-19 Diagnosis from Chest X-Ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super Resolution Convolutional Neural Network: Algorithm Development and Validation (Preprint)

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