COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network

Monday, Happy Nkanta; Li, Jian Ping; Nneji, Grace Ugochi; Nahar, Saifun; Hossin, Md Altab; Jackson, Jehoiada

doi:10.3390/healthcare10030422

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…Their model consists of four convolutional blocks where each is composed of a single convolutional, batch normalization, ReLU activation function, and max-pooling layer. Monday et al [62] proposed a neurowavelet capsule network. Firstly, they presented a multi-resolution analysis of a discrete wavelet transform to filter noisy and incompatible information from the CXR data to enhance the feature extraction robustness of the network.…”

Section: Review Of Literaturementioning

confidence: 99%

Challenges of deep learning methods for COVID-19 detection using public datasets

Hasan

Alam

Dahal

et al. 2022

Informatics in Medicine Unlocked

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Section: Review Of Literaturementioning

confidence: 99%

Challenges of deep learning methods for COVID-19 detection using public datasets

Hasan

Alam

Dahal

et al. 2022

Informatics in Medicine Unlocked

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“…Their developed scheme achieved 99% accuracy on a test set and 91.3% accuracy on a training set for super-pixel-based histone image characterization. Researchers in [ 14 ] classified COVID-19 positive cases by proposing a discrete-wavelet-transform-based neurowavelet capsule network that first minimized the noise present in X-ray images and then performed training for classification. They managed to obtain precision of 99.7%, sensitivity of 99.2%, and accuracy of 99.6%.…”

Section: Introductionmentioning

confidence: 99%

Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies

Rasheed

Shubair

2022

Healthcare

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The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance–minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier.

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“…Other testing methods include vision-based technology such as computed tomography (CT) imaging [6] and CXR imaging [7,8]. In a clinical review of COVID-19, CT and CXR scans have shown to be successful [9][10][11][12][13][14]. However, COVID-19 detection based on CT scan is time-consuming and requires experts' involvement.…”

Section: Introductionmentioning

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

Monday

Nneji

et al. 2022

Diagnostics

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Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. 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. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) 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. 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. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.

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COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network

Cited by 8 publications

References 59 publications

Challenges of deep learning methods for COVID-19 detection using public datasets

Challenges of deep learning methods for COVID-19 detection using public datasets

Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

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