COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network

Türkoğlu, Muammer

doi:10.1016/j.irbm.2021.01.004

Cited by 67 publications

(43 citation statements)

References 27 publications

(28 reference statements)

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“…The radiomics model of COVID-19 studies based on CT images involves predominantly of diagnostic and prognostic value; a majority of the recently published studies focus on the diagnosis and differentiation of COVID-19 such as those using UNet for automated detection of GGO areas [ 20 , 21 ] and differentiation of COVID-19 pneumonia from other viral pneumonia using radiomics or deep-learning methods [ 22 – 24 ]. However, to the best of our knowledge, there have been few studies on the validity of CT for assisting decision-making in the management of COVID-19 with regard to stratification of disease severity and prediction of clinical outcomes [ 18 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model

Zhao

Yang

et al. 2021

Stem Cells International

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The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists’ subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early ( n = 75 ), progressive ( n = 58 ), severe ( n = 75 ), and absorption ( n = 76 ) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K -best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f 1 -score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19.

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Section: Discussionmentioning

confidence: 99%

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model

Zhao

Yang

et al. 2021

Stem Cells International

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“…Nayak et al [ 4 ] demonstrated higher success compared to the model proposed in this study, but they used hold-out technique, which is called a simple technique, in their study. Moreover, the proposed model presented a significant performance improvement compared to the studies performed on the CT images except for the study of Turkoğlu [ 12 ]. In addition, some of the limitations of this study may be clear in future research.…”

Section: Discussionmentioning

confidence: 99%

“…In spite of the classification accuracy of the proposed model slightly low from the studies of Nayak et. [ 4 ] and Türkoğlu [ 12 ], it is successful compared to the studies of Sethy and Behera [ 49 ]. Nayak et al [ 4 ] demonstrated higher success compared to the model proposed in this study, but they used hold-out technique, which is called a simple technique, in their study.…”

Section: Discussionmentioning

confidence: 99%

“…Deep features obtained from pre-trained Convolutional Neural Networks (CNN) layers are widely used in the computer vision. Chest X-ray and computed tomography (CT) medical images are widely used to detect Covid-19 cases [ 2 , 4 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Then, the author voted the classification results obtained by different activation methods in the proposed model called Multiple Kernels-ELM-based Deep Neural Network (MKs-ELM-DNN) for detecting Covid-19 disease. According to the results obtained, the MKs-ELM-DNN model presented 98.36% accuracy [ 12 ]. Lastly, Nayak performed a contrastive analysis of eight pre-trained networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Akyol

Şen

2021

Interdiscip Sci Comput Life Sci

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Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.

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Detection of COVID‐19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra‐low‐dose CT images

Kannan

Karunambiga

Kumar³

et al. 2023

Concurrency and Computation

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SUMMARYIn this article, the detection of COVID‐19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra‐low‐dose CT (ULDCT) images is proposed. Here, the ultra‐low‐dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto‐encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI‐Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID‐19 ULDCT images classification as COVID‐19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN‐AOA‐ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%; precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet‐HHO‐ULDCT, ELM‐DNN‐ULDCT, EDL‐ULDCT, ResNet 50‐ULDCT, SDL‐ULDCT, CNN‐ULDCT, and DRNN‐ULDCT, respectively.

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COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network

Cited by 67 publications

References 27 publications

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Detection of COVID‐19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra‐low‐dose CT images

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