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The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will b...
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Coronavirus Disease 2019 (COVID-19) demonstrated the need for accurate and fast diagnosis methods for emergent viral diseases. Soon after the emergence of COVID-19, medical practitioners used X-ray and computed tomography (CT) images of patients' lungs to detect COVID-19. Machine learning methods are capable of improving the identification accuracy...
The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for th...
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... SVM searches for optimal hyperplane (decision boundary) separating the data of one class from another if the data feature and the target are linear. For non-linear separable patterns, SVM use a kernel function to transfer the provided pattern onto a new higher dimension space, where the pattern becomes linearly separable (Maia et al., 2020). Selecting different kernel function is an important aspect in the SVM-based classification. ...
... Moreover, of all the convolutional models provided by Sethy et al. [17], the ResNet50 model exhibited the best accuracy (98.66%), which used a support vector machine (SVM). Author Maia et al. [18] proposes a kernel-adjusted CNN model that uses a SVM model. The latter utilised a dataset of 437 photos and completed the task with 95.38% accuracy. ...
... The accuracy of the model for determining binary classification is 99.62%, and the accuracy for determining multiclass classification is 96.70%. Some of the above works [17], [18], [21], [22], [27], [37], [40] have yielded promising results on a small number of datasets, but much more work and testing are required before they can be put to use. Figure 1). ...
Despite significant efforts to devise a reliable method for COVID-19 diseases prediction, nature and mutation of the virus provide a considerable obstacle to accurately diagnosing the cases covered. A model that can reliably detect COVID-19 from chest X-ray images will greatly facilitate its early detection. The ability to process multiple images concurrently and the reliance on human supplied parameters in machine learning contribute to the deep learning techniques' popularity in the academic sector. Here, we present a hybrid CNN model for recognizing COVID-19 patients in chest X-ray images, built on top of transfer learning and image segmentation. The classifier was tested on 15153 chest X-ray pictures, of which 10192 showed pneumonia, 1345 were normal, and 3616 had COVID-The recommended model had a higher accuracy rate, at 98.34 percent. This research aims to identify COVID-19 early, minimizing its impact on the patient's body.
... SVM is a very popular and reliable prediction method. During the COVID-19 pandemic, its application has been confirmed for diagnostic purposes [22][23][24], mortality risk assessment [25,26], detecting undertriage in telephone triage [27], etc. ...
In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.
... In this study SVM model was configured with a linear kernel, and the regularization parameter C=1.0 was suggested by 44 ...
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. A dataset used is 960 slices of CT scan collected from Iraqi patients /Ibn Al-Nafis teaching hospital. The performance metrics are used in this study (accuracy, recall, precision, and F1 scores). The results indicate that the proposed approach generated a high-quality model for the collected dataset, with an overall accuracy of 98.95% and an overall recall of 97 %.
... According to the WHO, signs of a respiratory infection caused by the virus include inflammation of the lungs, fever, and coughing. Another aggravating issue is that the virus has a high personto-person transmission ratio [2], The best way to stop and postpone transmission is to keep your distance from people in social situations [3]. More than 150 million individuals had contracted the coronavirus by March 10, 2021, and 3 million people had died as a result of it [1]. ...
Due to increasing population, automated illness identification has become a critical problem in medical research. An automated illness detection framework aids physicians in disease diagnosis by providing precise, consistent, and quick findings, as well as lowering the mortality rate. Coronavirus (COVID-19) has expanded worldwide and is now one of the most severe and acute disorders. To avoid COVID-19 from spreading, making an automatic detection system based on X-ray chest pictures ought to be the quickest diagnostic alternative. The goal of this research is to come up with the best model for detecting COVID-19 diagnosis with the greatest accuracy. Therefore, four models, Convolutional Neural Networks, Residual Network 50, Visual Geometry Group 16 (VGG16), and VGG19, have been evaluated using the same images preprocessing method. In this study, performance metrics include accuracy, precision, recall, and F1 scores are used for evaluating proposed method. According to our findings, the VGG16 model is a viable candidate for detecting COVID-19 instances, because it has highest accuracy; in result overall accuracy of 98.44% in training phase, 98.05% invalidation phase and 96.05% in testing phase is obtained. The results of other performance measurements are shown in the result section, demonstrating that the majority of the approaches are more than 90% accurate. Based on these results, radiologists may find the proposed VGG16 model to be an intriguing and a helpful tool for detecting and diagnosing COVID-19 patients quickly.
... 2 | RELATED WORK Ahlawat and Choudhary (2020) Maia et al. (2020) developed a convolution support vector model for identifying the presence of coronavirus using X-ray images taken from infected patients. This model utilized SVM as the last layer for X-ray image classification. ...
This article offers a framework of a hybrid convolution neural network (CNN) – support vector machine (SVM) model for the classification of human metaphase chromosome images. In this model, the features extracted by CNN were fed to the SVM classifier to label the chromosomes into 24 classes. Classification accuracy is generally affected by low‐resolution chromosome images. Hence, in the proposed work, a Laplacian pyramidal super‐resolution network (LaPSRN) was deployed to improve input chromosome images resolution before feeding them to the classifier network. The experimentation showed that due to LaPSRN, resolution‐enhanced images are obtained, which has improved the classification accuracy by 3% compared to the architecture without a super‐resolution block. In addition, instead of using the usual ReLU function in CNN architecture, a Swish activation function was utilized, which enhanced the validation accuracy by 0.8%. After experimenting with various hyper‐parameters for the CNN SVM architecture, the ADAM optimizer with step decay outperformed the other standard optimizers. The novel part of this work is the combination of Laplacian pyramidal super‐resolution with the hybrid CNN‐SVM model, which has not been employed yet for chromosome classification tasks as per our knowledge. The proposed hybrid CNN SVM architecture with the Swish activation function and super‐resolution techniques was tested on the metaphase chromosome images from the BioImLab dataset, and an improved classification accuracy of 94.6% was obtained. Moreover, the standard metrics for classification like F1 score, precision, support, and the confusion matrix values of the proposed CNN model with SVM classifier are superior to the prevailing CNN architectures for chromosome images.
... As it takes up to 15 min to yourself understand a CT scan, images can be analyzed in 10s by using AI. 437 CT X-ray scans of healthy individuals, illnesses, and Covid-19 [24]. ...
The brand-new coronavirus COVID-19 spread rapidly over the globe and killed plenty of people. The World Health Organization (WHO) has classified COVID-19 as a global pandemic and all possible efforts are made to save human lives. Various governments implement complete and smart lockdowns to reduce COVID-19. For the detection and diagnosis of COVID-19, the conventional Polymerase Chain Reaction (PCR) test is regarded as essential. But an examination of a PCR test is time-consuming, requires resources as well as reports a large number of false negative results. Artificial Intelligence is fast growing technology with unlimited successful implementation with remarkable results in all problematic sectors. There are already several examples of AI-enabled Machine Learning (ML) and Deep Learning (DL) being used successfully in the healthcare industry. We have evaluated the most recent and cutting-edge research in this paper. ML and DL-based work for combating COVID-19. We discussed the COVID-19 background with all essentials and presented ML and DL-based systems highlighted by a new hierarchy. Identification and diagnosis of COVID-19 by considering medical images, sound analysis, and textual and non-invasive technologies. Further, we have also tabularized the COVID-19 datasets with their possible implementation. In last, we have listed the current challenges which are faced by ML and DL while combating COVID-19 and future directions for research.
... As a result, many researchers have proposed deep learning methods for detecting COVID-19 based on X-ray images [38,[40][41][42][43][44][45][46][47]. More information about other work methods for detecting COVID-19 using X-ray images is depicted in Table 11 (see in Appendix). ...
COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which we include a global average pooling layer, flattening, and two dense layers that are fully connected. The model’s effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model’s performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew’s correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic.
... e results show that, in 93.3% of cases, it can discriminate between cases of normal pneumonia and illnesses caused by viruses or bacteria other than COVID-19. e researchers have also used a CNN and support vector machine to find positive COVID-19 cases (SVM) [14]. is study's research team attained a 95.38 percent accuracy, sensitivity, and specificity. ...
It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model’s training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.
... Image annotation methods [21], active learning methods [22], and weakly supervised segmentation algorithms [23] have been proposed for alleviating the burden of manual annotation. As mentioned earlier, the approaches are applied to analyze natural scene images and not be straightforwardly applied in biomedical applications due to the limited availability of and large variations in biomedical training data [24][25][26][27]. Most of the approaches mentioned above do not explicitly exploit the interdependencies among annotations. ...
A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (?). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(?) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
