An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning

Özyurt, Fatih; Tuncer, Türker; Subaşı, Abdülhamit

doi:10.1016/j.compbiomed.2021.104356

Cited by 45 publications

(29 citation statements)

References 31 publications

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“…A four-phase method for COVID-19 detection is implemented by Ozyurt [ 74 ]. The feature extraction technique is emphasized by using techniques such as exemplar-based pyramid feature generation, ReliefF, and iterative principal component analysis (PCA) analysis.…”

Section: Summary Of the Research Methodsmentioning

confidence: 99%

A review of deep learning-based detection methods for COVID-19

Subramanian

Elharrouss

Al‐Maadeed

et al. 2022

Computers in Biology and Medicine

101

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COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.

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Section: Summary Of the Research Methodsmentioning

confidence: 99%

A review of deep learning-based detection methods for COVID-19

Subramanian

Elharrouss

Al‐Maadeed

et al. 2022

Computers in Biology and Medicine

101

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“…To find the remaining DNN hyperparameters, we computed the classification accuracy using 10-fold cross-validation for each manual formation. 39 This method is repeated for various sizes of hidden layer representations. Following this laborious manual procedure, the best classification result is obtained with a DNN composed of three hidden layers of 400, 180, and 40 nodes, respectively.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator

Liu¹,

Cui²,

Luo³

et al. 2022

IJGM

Self Cite

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Purpose Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances. Patients and Methods This work presents a new deep feature generation technique for breast cancer detection using BUS images. The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generation phase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal. Conclusion The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.

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“…Ozyurt et al. [39] used a traditional machine learning descriptor (LBP), and a feature selector method that selects most informative features together to achieve a better performance, achieving a 95.84% classification accuracy on CT images. Rahimzadeh and Attar [40] introduced a combined deep CNN to identify

chest X-ray images.…”

Section: Literature Reviewmentioning

confidence: 99%

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

Muhammad

Hoque

Oussalah

et al. 2022

Knowledge-Based Systems

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COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.

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An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning

Cited by 45 publications

References 31 publications

A review of deep learning-based detection methods for COVID-19

A review of deep learning-based detection methods for COVID-19

Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

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