MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images

Canayaz, Murat

doi:10.1016/j.bspc.2020.102257

Cited by 113 publications

(109 citation statements)

References 22 publications

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“…Canayaz in (Canayaz 2021 ) used feature extraction technique for image contrast enhancement. He used different deep learning models like AlexNet, GoogleNet, VGG19, and ResNet to complete the feature extraction.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

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The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

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“…A 22-layer CNN architecture was proposed by Hussain et al [ 20 ] which achieved a classification accuracy of 99.1%, 94.2%, and 91.2% for binary, 3-class, and 4-class classification, respectively. Canayaz et al [ 21 ] developed a model called MH-COVIDNet that used VGG19 as a feature extractor and BPSO meta-heuristic algorithm (MH algorithm) for feature selection. This approach obtained a classification accuracy of 99.38%.…”

Section: Related Workmentioning

confidence: 99%

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

Anis

Xuan

Chuah

et al. 2021

EURASIP J. Adv. Signal Process.

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Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

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“…Various ML algorithms for multiclass classification to differentiate COVID‐19 infections from other forms of pneumonia and healthy cases have been developed 54,55,100,102,104,105 . In one study, the authors developed a suitable model for training on a relatively limited data set that comprised 126 cases with COVID‐19, 14 with viral pneumonia, 41 with tuberculosis, 39 with bacterial pneumonia, and 134 normal cases 100 .…”

Section: For Image‐based Covid‐19 Diagnosis and Classificationmentioning

confidence: 99%

Machine learning for medical imaging‐based COVID‐19 detection and diagnosis

Rehouma

Büchert

Chen

2021

Int J of Intelligent Sys

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The novel coronavirus disease 2019 (COVID‐19) is considered to be a significant health challenge worldwide because of its rapid human‐to‐human transmission, leading to a rise in the number of infected people and deaths. The detection of COVID‐19 at the earliest stage is therefore of paramount importance for controlling the pandemic spread and reducing the mortality rate. The real‐time reverse transcription‐polymerase chain reaction, the primary method of diagnosis for coronavirus infection, has a relatively high false negative rate while detecting early stage disease. Meanwhile, the manifestations of COVID‐19, as seen through medical imaging methods such as computed tomography (CT), radiograph (X‐ray), and ultrasound imaging, show individual characteristics that differ from those of healthy cases or other types of pneumonia. Machine learning (ML) applications for COVID‐19 diagnosis, detection, and the assessment of disease severity based on medical imaging have gained considerable attention. Herein, we review the recent progress of ML in COVID‐19 detection with a particular focus on ML models using CT and X‐ray images published in high‐ranking journals, including a discussion of the predominant features of medical imaging in patients with COVID‐19. Deep Learning algorithms, particularly convolutional neural networks, have been utilized widely for image segmentation and classification to identify patients with COVID‐19 and many ML modules have achieved remarkable predictive results using datasets with limited sample sizes.

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MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images

Cited by 113 publications

References 22 publications

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

Machine learning for medical imaging‐based COVID‐19 detection and diagnosis

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