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1.
International Journal of Imaging Systems and Technology ; JOUR
Article in English | Web of Science | ID: covidwho-2082409

ABSTRACT

Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at .

2.
International Journal of Imaging Systems & Technology ; : 1, 2022.
Article in English | Academic Search Complete | ID: covidwho-2059446

ABSTRACT

Coronavirus disease 2019 (COVID‐19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content‐aware pre‐activated residual UNet (CAPA‐ResUNet), was proposed for segmenting COVID‐19 lesions from CT slices. In this network, the pre‐activated residual block was used for down‐sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID‐19 Lung CT Lesion Segmentation Challenge—2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA‐ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content‐aware residual UNet (CARes‐UNet). The code is available at https://github.com/malu108/LungInfectionSeg. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

3.
2nd International Conference of Smart Systems and Emerging Technologies, SMARTTECH 2022 ; : 12-13, 2022.
Article in English | Scopus | ID: covidwho-2018983

ABSTRACT

The novel coronavirus disease (COVID-19) constitutes a public health emergency globally. It is a deadly disease which has infected more than 230 million people worldwide. Therefore, early and unswerving detection of COVID-19 is necessary. Evidence of this virus is most commonly being tested by RT-PCR test. This test is not 100% reliable as it is known to give false positives and false negatives. Other methods like X-Ray images or CT scans show the detailed imaging of lungs and have been proven more reliable. This paper compares different deep learning models used to detect COVID-19 through transfer learning technique on CT scan dataset. VGG-16 outperforms all the other models achieving an accuracy of 85.33 % on the dataset. © 2022 IEEE.

4.
Medical Science ; 26(124):9, 2022.
Article in English | Web of Science | ID: covidwho-1980058

ABSTRACT

Introduction: CT chest is strongly recommended for evaluation in COVID-19 cases as it involves the respiratory system. In the current study, we correlate the CT chest with the most commonly encountered laboratory abnormalities in COVID-19 patients based on their CT severity grade. Materials and methods: This was a retrospective study, conducted in a designated COVID center in 123 hospitalized patients who were confirmed COVID-19 positive. The research was conducted over three months (August 2020 to October 2020). Patient demographics, chest CT findings with CT severity scores of the affected lung parenchyma, and laboratory values like serum D-dimer, CRP, ferritin, and lymphocyte count were reported. The association between the severity of a chest CT scan and the levels of laboratory parameters was investigated. Before the study, the local ethics committee granted its approval. Results: There were total of 123 cases, out of which 86 (30.1%) study subjects were males and 37 (69.9%) were females. There was no discernible link between gender and severity score. A positive correlation was seen between the CT imaging findings and serum D-dimer, CRP, and ferritin levels;however, a negative correlation was seen with lymphocyte count. Conclusion: A significant correlation is seen between the CT severity score with laboratory values and the disease severity. Chest CT score is an important signal of the amount of systemic inflammation and can help speed up the diagnostic procedure in symptomatic patients.

5.
Medical Science ; 26(124):9, 2022.
Article in English | Web of Science | ID: covidwho-1980054

ABSTRACT

Context: The currently on-going COVID-19 pandemic resulted in the abnormal lung parenchymal changes which can also alter pulmonary vascular hemodynamics. Aims: This study was aimed to assess CT derived pulmonary vascular indices in COVID-19 patients in different groups based on the extent of pneumonia using CT severity score. Settings and design: Retrospective study at COVID-19 care centre in central India. Methods and material: 'this study included 78 institutionalized patients who were confirmed COVID-19 positive status. All patients were assessed based on demographic data, CT severity score;CT derived pulmonary vascular indices such as main pulmonary artery diameter and the pulmonary artery to aorta ratio (PA/AO). Changes in these pulmonary vascular indices were determined in each mild, moderate and severe group of pneumonia. Results: Out of 78 patients, 25.6% patients belonged to mild group, 28.2% belonged to moderate group and 21.8% belonged to severe group. 70.5% of all patients were males and 29.5% were females. 11% males and 17.4% females showed increased pulmonary artery above normal limits. 4 males and 4 females with increased pulmonary artery diameter belonged to severe group of COVID 19 pneumonia while 8 out of 10 patients with increased PA/AO belonged to severe group of pneumonia extent. Conclusions: In this study, patients with pulmonary artery enlargement and increased PA/AO (PA/AO) were predominantly found to belong to severe group of COVID-19 pneumonia, a finding requiring further investigation which will help to predict pulmonary hypertension in COVID-19 patients which has an unfavourable outcome.

6.
BMC Med Imaging ; 21(1): 112, 2021 07 15.
Article in English | MEDLINE | ID: covidwho-1312621

ABSTRACT

BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists' experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. METHODS: We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. RESULTS: The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. CONCLUSION: The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement.


Subject(s)
Deep Learning , Lung/diagnostic imaging , Tomography, X-Ray Computed , Algorithms , Humans , Lung/anatomy & histology , Lung Diseases/diagnostic imaging , Lung Diseases/pathology , Neural Networks, Computer
7.
Quant Imaging Med Surg ; 12(10): 4758-4770, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1969928

ABSTRACT

Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data. Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models. Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability. Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, which may facilitate individualized management of patients with this disease.

8.
Appl Soft Comput ; 128: 109401, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1966354

ABSTRACT

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral-temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral-temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral-temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral-temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral-temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral-temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

9.
Concurr Comput ; 34(23): e7211, 2022 Oct 25.
Article in English | MEDLINE | ID: covidwho-1966037

ABSTRACT

A novel corona virus (COVID-19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID-19. To predict the patients of COVID-19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya-tunicate swarm algorithm driven generative adversarial network (Jaya-TSA with GAN) is proposed in this research to find patients of COVID-19 infections. The developed Jaya-TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID-19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya-TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya-TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.

10.
Journal of Electronic Science and Technology ; : 100161, 2022.
Article in English | ScienceDirect | ID: covidwho-1914692

ABSTRACT

Corona Virus Disease 2019 (COVID-19) has affected millions of people worldwide and caused more than 3.9 million deaths [1]. Increased attempts have been made to develop deep learning methods to diagnosis COVID-19 based on computed tomography (CT) lung images. It is a challenge to reproduce and obtain the CT lung data because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task. ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also, VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, is employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique (ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks which compromises of, end-to-end, VGG16, ResNet50, and U-Net with VGG16 or ResNet50 are applied on the dataset which is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieve the best performance. The proposed classification model achieves 98.98% accuracy(ACC), 98.87% area under the ROC curve (AUC), 98.89% sensitivity (Se), 97.99% precision (Pr), 97.88% F1- score, and 1.8974-seconds computational time.

11.
Oper Tech Otolayngol Head Neck Surg ; 33(2): 147-157, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1900067

ABSTRACT

There is a wide spectrum of clinical manifestation of COVID-19 in the head and neck, but often these do not have an imaging correlate. This review will highlight the most common imaging features of COVID-19 in the head and neck that can be seen on routine head and neck CT and MRI. In addition, situations where a more dedicated imaging protocol is required will be highlighted. Finally, as mass vaccination efforts are underway worldwide, post vaccination imaging can often complicate cancer surveillance imaging. Post vaccination imaging features and recommendations will be discussed.

12.
6th International Conference on Trends in Electronics and Informatics, ICOEI 2022 ; : 1591-1597, 2022.
Article in English | Scopus | ID: covidwho-1901461

ABSTRACT

The impact of COVID-19 is severe worldwide;detecting the Covid severity in a patient is a vital step. The further important actions such as isolating the patient from others and testing the people in frequent contact with the patient can only be done after the Covid-19 test results. Currently, different methods are used for detecting the Corona virus in a patient, they are Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test, Rapid Diagnostic Test (RDT), and Computed Tomography (CT) scan for lungs. However, a CT scan is the most accurate way to detect covid compared to other tests. The CT scan can produce images of the lungs within 15 to 20 minutes. Whereas traditional methods such as RT-PCR will take at least six to eight hours to deliver results. This paper aims to determine the severity level of Covid from the Computed Tomography (CT) scan image of the lungs. © 2022 IEEE.

13.
Cureus ; 14(5): e25319, 2022 May.
Article in English | MEDLINE | ID: covidwho-1897136

ABSTRACT

INTRODUCTION:  Pulmonary hypertension (PH) is a threatening condition, and it is far more common than previously assumed, especially after the COVID pandemic. Its outcome is not good; if detected late, and can lead to right ventricular failure, which can be fatal. Our goal was to evaluate CT signs of PH, correlate them with echocardiography, and identify the cut-off values of these signs in our population. METHOD:  In this study, 160 patients having both CT and echocardiography with a maximum gap of one month were assessed from June to November 2021. The association between CT signs and echocardiography to diagnose PH was investigated. The Pearson and Spearman correlation and area under receiver operating curve (AUROC) tests were performed in the analysis. Receiver operating characteristic curve analysis was also used to assess CT's diagnostic capability and cut-off values. RESULT:  The correlation between main pulmonary artery (MPA) diameter and main pulmonary artery to aorta ratio (MPA/AO) with mean pulmonary artery pressure (mPAP) was weak but statistically significant (r = 0.316 and r = 0.321, p<0.001). However, there was a very weak correlation between the right and left pulmonary artery and mPAP with correlation coefficients (r) of 0.155 and 0.138, respectively. For the first time in our population, we measured the cut-off values of MPA and MPA/AO ratios for PH which were 26 and 0.88 mm, respectively. CONCLUSIONS:  The CT signs of PH correlate with echocardiography; however, should not be used solely; the cut-off values should be used according to race and population.

14.
Periodicals of Engineering and Natural Sciences ; 10(2):376-387, 2022.
Article in English | Scopus | ID: covidwho-1863533

ABSTRACT

The new coronavirus disease (2019) has spread quickly as an acute respiratory distress syndrome (ARDS) among millions of individuals worldwide. Furthermore, the number of COVID-19 checking obtainable in hospitals is very limited as compared to the rising number of infections every day. As an outcome, an automatic detection system must be implemented as a quick diagnostic tool for preventing or reducing the spread of COVID-19 among humans. The present paper aims to propose an automated system by means of a hybrid Deep Learning ("convolutional neural network "(CNN)) and "support vector machine (SVM) " approach for identifying COVID-19 pneumonia-infected patients on the basis of chest computed tomography (746 CT images of "COVID-19" and "non-COVID-19"). The proposed system is composed of three phases. The first, pre-processing phase begins with converting CT images into greyscale level CT images of equal size (256×256). The "contrast limited adaptive histogram equalization" technology is adopted to enhance the intensity levels, and demonstrate the feature of lung tissue. It is also necessary to normalize the division of the image elements by 255 to make the values between 0 and 1, as this will speed up the processing process. The second phase, the CNN (SimpNet model), was applied as a deep feature extraction technique to identify CT samples. The SVM classifier and SoftMax function are employed in the third phase to classify COVID-19 pneumonia-infected patients. Specificity, Sensitivity, "F-score ", Accuracy, and "area under curve" are used as criteria to estimate the efficiency of the classification. The results showed a high accuracy rate of COVID-19 classification which reached (98%) and (99.1%) for CNN-SoftMax and CNN-SVM classifier, respectively in the tested dataset (225 CT images). © The Author 2022. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal.

15.
J Clin Imaging Sci ; 12: 6, 2022.
Article in English | MEDLINE | ID: covidwho-1856606

ABSTRACT

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

16.
Cureus ; 14(4): e23810, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1835790

ABSTRACT

Tracheomegaly is a medical condition where the tracheal diameter is greater than the upper limits of normal. Tracheomegaly can be classified as primary or secondary. Primary tracheomegaly is usually congenital. Secondary tracheomegaly can be due to multiple causes, including connective tissue disease, infections, autoimmune diseases like sarcoidosis, and prolonged mechanical ventilation. Here, we describe the first reported case of tracheomegaly secondary to coronavirus disease 2019 (COVID-19) pneumonia and COVID-induced interstitial lung disease (ILD). While many cases of tracheomegaly are asymptomatic, patients can have symptoms like cough, dyspnea, hemoptysis, or even respiratory failure. Tracheomegaly is associated with a higher risk of recurrent lower respiratory tract infections, chronic cough, bronchiectasis, and tracheobronchomalacia. Early recognition of COVID-19-induced tracheomegaly can help initial early management and reduce the incidence of infections.

17.
Computer Journal ; : 10, 2022.
Article in English | Web of Science | ID: covidwho-1821728

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has been a globally dangerous crisis that causes an increasingly high death rate. Applying machine learning to the computed-tomography (CT)-based COVID-19 diagnosis is essential and attracts the attention of the research community. This paper introduces an approach for simultaneously identifying COVID-19 disease and segmenting its manifestations on lung images. The proposed method is an asymmetric U-Net-like model improved with skip connections. The experiment was conducted on a light-weighted feature extractor called CRNet with a feature enhancement technique called atrous spatial pyramid pooling. Classifying between COVID-19 and non-COVID-19 cases recorded the highest mean scores of 97.1, 94.4, and 97.0% for accuracy, dice similarity coefficient (DSC) and F1 score, respectively. Alternatively, the respective highest mean scores of the classification between COVID-19 and community-acquired pneumonia were 99.89, 99.79, and 99.97%. The lesion segmentation performance was with the highest mean of 99.6 and 84.7% for, respectively, accuracy and DSC.

18.
Traitement Du Signal ; 39(1):205-219, 2022.
Article in English | Web of Science | ID: covidwho-1791615

ABSTRACT

Since the end of 2019, a COVID-19 outbreak has put healthcare systems worldwide on edge. In rural areas, where traditional testing is unfeasible, innovative computer-aided diagnostic approaches must deliver speedy and cost-effective screenings. Conducting a full scoping review is essential for academics despite several studies on the use of Deep Learning (DL) to combat COVID-19. This review examines the application of DL techniques in CT and ULS images for the early detection of COVID-19. In this review, the PRISMA literature review approach was followed. All studies are retrieved from IEEE, ACM, Medline, and Science Direct. Performance metrics were highlighted for each study to measure the proposed solutions' performance and conceptualization;A set of publicly available datasets were appointed;DL architectures based on more than one image modality such as CT and ULS are explored. Out of 32 studies, the combined U-Net segmentation and 3D classification VGG19 network had the best F1 score (98%) on ultrasound images, while ResNet-101 had the best accuracy (99.51%) on CT images for COVID-19 detection. Hence, data augmentation techniques such as rotation, flipping, and shifting were frequently used. Grad-CAM was used in eight studies to identify anomalies on the lung surface. Our research found that transfer learning outperformed all other AI-based prediction approaches. Using a UNET with a predefined backbone, like VGG19, a practical computer-assisted COVID-19 screening approach can be developed. More collaboration is required from healthcare professionals and the computer science community to provide an efficient deep learning framework for the early detection of COVID-19.

19.
Journal of Image and Graphics ; 27(3):722-749, 2022.
Article in Chinese | Scopus | ID: covidwho-1789678

ABSTRACT

Lung disease like corona virus disease 2019(COVID-19) and lung cancer endanger the health of human beings. Early screening and treatment can significantly decrease the mortality of lung diseases. Computed tomography (CT) technology can be an effective information collection method for the diagnosis and treatment of lung diseases. CT-based lung lesion region image segmentation is a key step in lung disease screening. High quality lung lesion region segmentation can effectively improve the level of early stage diagnosis and treatment of lung diseases. However, high-quality lung lesion region segmentation in lung CT images has become a challenging issue in computer-aided diagnosis due to the diversity and complexity of lung diseases. Our research reviews the relevant literature recently. First, it is compared and summarized the pros and cons of traditional segmentation methods of lung CT image based on region and active contour. The region-based method uses the similarity and difference of features to guide image segmentation, mainly including threshold method, region growth method, clustering method and random walk method. The active-contour-based method is to set an initial contour line with decreasing energy. The contour line deforms in the internal energy derived from its own characteristics and the external energy originated from image characteristics. Its movement is in accordance with the principle of minimum energy until the energy function is in minimization and the contour line stops next to the boundary of lung region. The active contour method is divided into parametric active contour method and geometric active contour method in terms of the contour curve analysis. Low segmentation accuracy lung CT image segmentation methods are widely used in the early stage diagnosis. Next, the improved model analysis of lung CT image segmentation network structure is based on convolutional neural networks (CNNs), fully convolutional networks (FCNs), and generative adversarial network (GAN). In respect of the CNN-based deep learning segmentation methods, the segmentation methods of lung and lung lesion region can be divided into two-dimensional and three-dimensional methods in terms of the dimension of convolution kernel, the segmentation methods of lung and lung lesion region can also be divided into two-dimensional and three-dimensional methods based on the dimension of convolution kernel for the FCN-based deep learning segmentation methods. In respect of the U-Net based lung CT image segmentation methods, it can be divided into solo network lung CT image segmentation method and multi network lung CT image segmentation method according to the form of U-Net architecture. Due to the CT image containing COVID-19 infection area is very different from the ordinary lung CT imageand the differentiated segmentation characteristics of the two in the same network, the solo network lung CT image segmentation method can be analyzed that whether the data-set contains COVID-19 or not. The multi-network lung CT image segmentation method can be divided into cascade U-Net and dual path U-Net based on the option of serial mode or parallel mode. For the GAN-based lung CT image segmentation methods, it can be divided into GAN models based on network architecture, generator and other methods according to the ways to improve the different architectures of GAN. Deep-learning-based segmentation method has the advantages of high segmentation accuracy, strong transfer learning ability and high robustness. In particular, the auxiliary diagnosis of COVID-19 cases analysis is significantly qualified based on deep learning. Next, the common datasets and evaluation indexes of lung and lung lesion region segmentation are illustrated, including almost 10 lung CT open datasets, such as national lung screening test(NLST) dataset, computer vision and image analysis international early lung cancer action plan database(VIA/I-ELCAP) dataset, lung image database consortium and image database resource initiative(LIDC-IDRI) dataset and Nederlands-Leuvens Long anker Screenings Onderzoek(NELSON) dataset, and 7 COVID-19 lung CT datasets analysis. It also demonstrates that the related lung CT images datasets is provided based on five large-scale competitions, including TIANCHI dataset, lung nodule analysis 16(LUNA16) dataset, Lung Nodule Database(LNDb) dataset, Kaggle Data Science Bowl 2017(Kaggle DSB) 2017 dataset and Automatic Nodule Detection 2009(ANODE09) dataset, respectively. Our 8 evaluation index is commonly used to evaluate the quality of lung CT image segmentation model, including involved Dice similarity coefficient, Jaccard similarity coefficient, accuracy, precision, false positive rate, false negative rate, sensitivity and specificity, respectively. To increase the number and diversity of training samples, GAN is used to synthesize high-quality adversarial images to expand the dataset. At the end, the prospects, challenges and potentials of CT-based high-precision segmentation strategies are critical reviewed for lung and lung lesion regions. Because the special structure of U-Net can effectively extract target features and restore the information loss derived from down sampling, it does not need a large number of samples for training to achieve high segmentation effect. Therefore, it is necessary to segment lung and lung lesions based on U-Net. The integration of GAN and U-Net is to improve the segmentation accuracy of lung and lung lesion areas. GAN-based network architecture is to extend the dataset for good training quality. The further U-Net application has its priority for qualified segmentation consistently. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

20.
2022 International Conference for Advancement in Technology, ICONAT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1788716

ABSTRACT

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is the main cause of Corona virus disease 2019 (COVID-19) resulting in a massive death toll across the world. In December 2019, Wuhan Province of China witnessed the first case of COVID-19 and within less time complete world suffered from this deadly virus. Medical imaging modalities like X-ray, Computed Tomography (CT), Medical Resonance Image (MRI) etc. plays vital role in detecting COVID-19. Further medical imaging when combined with the recently emerging technologies - Artificial Intelligence (AI), Deep Learning and Machine Learning (ML) strengthens the power of the imaging tools and help medical specialists for diagnosis. Moreover, the Computer Aided Diagnosis (CAD) platforms can also be developed to help radiologists make clinical decisions. This paper can provide the researchers and organizations with new insights in how the medical imaging along with recent technologies can aid to overcome the situation of COVID-19 by detecting and diagnosing in its early stage. © 2022 IEEE.

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