A comparative study of X-ray and CT images in COVID-19 detection using image processing and deep learning techniques

Shyni, Dr. H Mary; Ekambaram, Chitra

doi:10.1016/j.cmpbup.2022.100054

Cited by 37 publications

(12 citation statements)

References 78 publications

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“…CNNs' role in X-ray-based COVID-19 detection. Their findings substantiate the intricate nature of the virus detectable through medical imaging, emphasizing the importance of DL methodologies in the process[7]. Another pivotal approach in this scientific endeavor is the exploration of Transfer Learning (TL).…”

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confidence: 71%

Readiness Assessment of Public University in Bangladesh for Education 4.0: A Case Study of Shahjalal University of Science and Technology – Sylhet, Bangladesh

Islam

2022

Khulna Univ. Stud.

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In order to get the benefits of Industry 4.0 (4ir) we need the graduates who are able to face the challenges of 4ir. As the universities are the main sources of producing graduates, they should produce adaptable graduates of 4ir. The 4ir demands such a system of higher education where each and every stakeholders especially educators and learners should be connected in a globalized automated environment, networked, virtualized and flexible. In the paradigm of 4ir learners should learn to learn the sources of learning how to build knowledge and skills within the process of teaching –learning. The study aims to identify the dimensions of E4.0 corresponding to 4ir and to assess the readiness of Shahjalal University of Science and Technology (SUST) is one of 50 public universities of Bangladesh. At present there are 28 disciplines in six schools of studies in SUST and among them 20 departments have been selected for the study based on minimum 10 years of establishment. The assessment has been focused on four aspects mainly curriculum, teacher (faculty), intuitional qualification and logistic supports in terms of E4.0. In conducting the study data from collected 120 teachers and graduate students with a structured questionnaire. The questionnaire was five point scales. The collected data was analyzed with SPSS and mainly frequency in each dimension was found and finally factor analysis was done to find the factors affecting E4.0. The frequency distribution showed that Robotic Process Automation aspects ranked first following cloud computing, data analytics, IoT, AI, and BCT. The factor analysis showed that seventeen aspects have been considered as important and the top most five aspects are fees and rewards, data as a service, software as a service, AI extraction and user device.

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confidence: 71%

Readiness Assessment of Public University in Bangladesh for Education 4.0: A Case Study of Shahjalal University of Science and Technology – Sylhet, Bangladesh

Islam

2022

Khulna Univ. Stud.

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“…COVID-19 detection approaches relying on TL techniques having been widely implemented in literature considering various networks such as AlexNet, VGG, ResNet, MobileNet, Inception, and DenseNet [ 6 , 45 ]. Ahuja et al [ 7 ] compared three pre-trained variants of ResNet to SqueezeNet for COVID-19 detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms

Abdel-Hamid

2023

Multimed Tools Appl

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Chest computer tomography (CT) provides a readily available and efficient tool for COVID-19 diagnosis. Wavelet and contourlet transforms have the advantages of being localized in both space and time. In addition, multiresolution analysis allows for the separation of relevant image information in the different subbands. In the present study, transform-based features were investigated for COVID-19 classification using chest CT images. Several textural and statistical features were computed from the approximation and detail subbands in order to fully capture disease symptoms in the chest CT images. Initially, multiresolution analysis was performed considering three different wavelet and contourlet levels to determine the transform and decomposition level most suitable for feature extraction. Analysis showed that contourlet features computed from the first decomposition level (L1) led to the most reliable COVID-19 classification results. The complete feature vector was computed in less than 25 ms for a single image having of resolution 256 × 256 pixels. Next, particle swarm optimization (PSO) was implemented to find the best set of L1-Contourlet features for enhanced performance. Accuracy, sensitivity, specificity, precision, and F-score of a 100% were achieved by the reduced feature set using the support vector machine (SVM) classifier. The presented contourlet-based COVID-19 detection method was also shown to outperform several state-of-the-art deep learning approaches from literature. The present study demonstrates the reliability of transform-based features for COVID-19 detection with the advantage of reduced computational complexity. Transform-based features are thus suitable for integration within real-time automatic screening systems used for the initial screening of COVID-19.

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“…83 Generally, x-ray images are not preferred by researchers for detecting COVID-19 as they do not provide 3D information and the quality of the image is low. 84 Due to information degradation of the images in xray, it is hard to enhance the accuracy level in the prediction process. Hence, the CT images are preferable to identify the COVID-19 disease to avoid the accuracy reduction.…”

Section: X-ray Imagesmentioning

confidence: 99%

COVID‐19: A systematic review of prediction and classification techniques

Varma,

Kalra,

Kirmani

2023

Int J Imaging Syst Tech

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COVID‐19 has affected more than 760 million people all over the world, as per the latest record of the WHO. The rapid proliferation of COVID‐19 patients not only created a health emergency but also led to an economic crisis. An early and accurate diagnosis of COVID‐19 can help in combating this deadly virus. In line with this, researchers have proposed several machine learning (ML) and deep learning (DL) techniques for detecting COVID‐19 since 2020. This article presents currently available manual diagnosis methods along with their limitations. It also provides an extensive survey of ML and DL techniques that can support medical professionals in the precise diagnosis of COVID‐19. ML methods, namely K‐nearest neighbor, support vector machine (SVM), artificial neural network, decision tree, naive bayes, and DL methods, viz. deep neural network, convolutional neural network (CNN), region‐based convolutional neural network, and long short‐term memories, are explored. It also provides details of the latest COVID‐19 open‐source datasets, consisting of x‐ray and computed tomography scan images. A comparative analysis of ML and DL techniques developed for COVID‐19 detection in terms of methodology, datasets, sample size, type of classification, performance, and limitations is also done. It has been found that SVM is the most frequently used ML technique, while CNN is the most commonly used DL technique for COVID‐19 detection. The challenges of an existing dataset have been identified, including size and quality of datasets, lack of labeled datasets, severity level, data imbalance, and privacy concerns. It is recommended that there is a need to establish a benchmark dataset that overcomes these challenges to enhance the effectiveness of ML and DL techniques. Further, hurdles in implementing ML and DL techniques in real‐time clinical settings have also been highlighted. In addition, the motivation noticed from the existing methods has been considered for extending the research with an optimized DL model, which attained improved performance using statistical and deep features. The optimized deep model performs better than 90% based on efficient features and proper classifier tuning.

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A comparative study of X-ray and CT images in COVID-19 detection using image processing and deep learning techniques

Cited by 37 publications

References 78 publications

Readiness Assessment of Public University in Bangladesh for Education 4.0: A Case Study of Shahjalal University of Science and Technology – Sylhet, Bangladesh

Readiness Assessment of Public University in Bangladesh for Education 4.0: A Case Study of Shahjalal University of Science and Technology – Sylhet, Bangladesh

Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms

COVID‐19: A systematic review of prediction and classification techniques

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