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1.
Math Biosci Eng ; 20(4): 6612-6629, 2023 02 02.
Article in English | MEDLINE | ID: covidwho-2238681

ABSTRACT

OBJECTIVE: To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients. METHOD: We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set. RESULT: For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set. CONCLUSION: Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.


Subject(s)
COVID-19 , Fatty Liver , Humans , COVID-19/diagnostic imaging , COVID-19/epidemiology , Retrospective Studies , Thymus Gland/diagnostic imaging , Disease Progression
2.
2nd International Conference on Technological Advancements in Computational Sciences, ICTACS 2022 ; : 857-862, 2022.
Article in English | Scopus | ID: covidwho-2213301

ABSTRACT

The aim of this analysis is to identify the textural alterations due to incidence of COVID-19 in lung CT scan images using GLCM matrix in comparison with GLRLM. Materials and Methods: Sample size is calculated using G power analysis and a total of 176 sample sizes are acquired for this novel texture analysis using parameters like effect size (0.3), standard error rate (0.05), maximum rate (0.8) and allocation rate (N2/N1=1). For this analysis the required CT images are collected from Github. For group 1 a total of 94 sample images are taken and for group 2 a total of 82 sample images are taken. For analyzing the textural alterations of CT scan lung images, comparison between Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is carried out for this analysis. In the process of evaluation of classifiers 10-fold cross validation is performed. Normal and COVID subjects are classified using Random forest, K-NN, Logistic regression classifiers for better classification. Results and Discussion: Due to incidence of COVID in lunge tissues it is observed that textural alterations are formed in lung CT scan images. From the acquired features values of GLCM and GLRLM it is observed that GLCM is statistically significant than the GLRLM. Contrast, homogeneity and sum of average features are statistically significant (0.0001) in identifying normal and COVID subjects. The mean value of homogeneity for healthy controls is (0.215) and for COVID subjects it is (0.327) such that normal subjects have a gentle surface of the lung and COVID subjects have rough surface and significance value is (p<0.05). GLCM has acquired precision (0.931), F1-score (0.928), Recall (0.929), AUC (0.981), Classification Accuracy (0.929) are obtained using random forest classifiers. From the above values it is observed that COVID subjects have textural variations than the normal subjects. Conclusion: From this analysis it is observed that GLCM provides significantly better classification in differentiating the COVID and normal subjects than GLRLM. © 2022 IEEE.

3.
17th IEEE International Conference on Computer Science and Information Technologies, CSIT 2022 ; 2022-November:551-554, 2022.
Article in English | Scopus | ID: covidwho-2213172

ABSTRACT

The current study considers the development of a 5-layer pipeline for identifying and classifying COVID-19-induced lung lesions. Such system is multilayer, built upon convolutional and fully connected neural networks and logistic self-organised forest built using the group method of data handling (GMDH) principles. This pipeline includes a mechanism for finding lesions regions in lungs computer tomography images and for calculating related lung damage volume. The layer for finding images with lesions reached a Matthews Correlation Coefficient of 0.98. The layer for lesions segmentation reached a Dice similarity coefficient of 0.74, while the layer for lesions classification reached Fl-scores of 1, 0.95, 0.93 respectively for the ground-glass, opacity, crazy-paving and consolidation lesion type. Results demonstrate the effectiveness of the implemented multi-layer system in solving tasks of lesions identification and classification while being composed into a single pipeline. © 2022 IEEE.

4.
Chemometr Intell Lab Syst ; 233: 104750, 2023 Feb 15.
Article in English | MEDLINE | ID: covidwho-2165147

ABSTRACT

Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.

5.
6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022 ; : 393-397, 2022.
Article in English | Scopus | ID: covidwho-2051962

ABSTRACT

This paper describes research on texture feature extraction for COVID-19 detection. Fractal Dimension Texture Analysis (FDTA) and Gray Level Co-occurrence Matrix (GLCM) were used for feature extraction. A dense neural network is used for classification. Three classes were used for classification to classify Normal, COVID-19, and Other pneumonia. The data entered in the texture feature extraction is a chest x-ray (CXR) image that is grey scaled and resized into 400400 pixels. Performance analysis of the model uses a confusion matrix. The best performance feature extraction method for detecting COVID-19 is FDTA, with an accuracy testing of 62.5%. © 2022 IEEE.

6.
Annals of Clinical and Analytical Medicine ; 13(8):831-835, 2022.
Article in English | Web of Science | ID: covidwho-2033342

ABSTRACT

Aim: In this study, we aimed to show the contribution of the chest computed tomography (CT)-based histogram analysis method, which will enable us to make quick decisions for patients who are clinically suspected of having COVID-19 infection and whose diagnoses cannot be confirmed by polymerase chain reaction (PCR) tests. Material and Methods: A total of 84 patients, 40 in the PCR-positive group (age range: 17-90 years) and 44 in the PCR-negative group (age range: 15-75 years), were included in the study. A total of 154 lesions with ground-glass density, 78 in the PCR-positive group and 76 in the PCR-negative group, were detected in these patients' thorax CT scans. The region of interest was placed on the ground-glass opacities from the images and numerical data were obtained by histogram analysis. Numerical data were uploaded to the MATLAB program. Results: The localizations of ground-glass densities in the CT findings of patients with probable and definite COVID-19 diagnoses were similar;74.7% of the ground-glass areas in both groups showed peripheral distribution. Lesions were frequently observed in right lungs and lower lobes. In histogram analysis, standard deviation, variance, size %L, size %M, and kurtosis values were higher in the PCR-positive than the PCR-negative group. When receiver operating characteristic curve analysis was performed for standard deviation values, the area under the curve was 0.640, and when the threshold value was selected as 123.4821, the two groups could be differentiated with 62.8% sensitivity and 61.8% specificity. Discussion: The use of histogram-based tissue analysis, which is a subdivision of artificial intelligence, for clinically highly suspicious patients increases the diagnostic accuracy of CT. Therefore, performing CT analysis with the histogram method will significantly aid healthcare professionals, especially in clinics where rapid decisions are required, such as in emergency services.

7.
Sensors (Basel) ; 22(16)2022 Aug 13.
Article in English | MEDLINE | ID: covidwho-2024040

ABSTRACT

As obesity is a serious problem in the human population, overloading of the horse's thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse's back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.


Subject(s)
Sports , Thermography , Animals , Back , Biomechanical Phenomena , Body Weight , Entropy , Horses , Humans
8.
13th Biomedical Engineering International Conference, BMEiCON 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1806884

ABSTRACT

Since its discovery in late 2019, COVID-19 has become a major worldwide concern due to its incredibly high degree of contagion, and early diagnosis is crucial to limit this global progression. Computed Tomography (CT) scans of the chest offer a low-cost alternative diagnosis modality to the standard reverse polymerase chain reaction (RT-PCR) test for COVID-19. In this paper, we analyze texture features extracted from chest CT scans using Gray Level Run Length Matrix (GLRLM) techniques for their ability to distinguish between COVID-19 and non-COVID-19 patients. Quantitative texture analysis of CT scans provides a measure of the biological heterogeneity in tissue microenvironment which can be useful in the diagnosis of a wide range of diseases, and we hypothesize that GLRLM texture features may hold significance for diagnosis of COVID-19. 13 GLRLM features were extracted from CT scans of 349 positive COVID-19 cases and 397 negative COVID-19 cases. Holdout validation was used to randomly split 70% of the images for training, and the remaining 30% for testing. A GentleBoost classifier was used to evaluate performance. A significant AUROC of 0.92 along with a high classification accuracy of 85.7% was obtained on the independent test set, indicating that GLRLM texture features extracted from chest CT scans have the potential to be a significant tool in the rapid and accurate diagnosis of COVID-19. © 2021 IEEE.

9.
Conference Applications of Digital Image Processing XLIV ; 11842, 2021.
Article in English | Web of Science | ID: covidwho-1745847

ABSTRACT

Palmprints are of considerable interest as a reliable biometric, since they offer significant advantages, such as greater user acceptance than fingerprint or iris recognition. 2D systems can be spoofed by a photograph of a hand;however, 3D avoids this by recovering and analysing 3D textures and profiles. 3D palmprints can also be captured in a contactless manner, which is critical for ensuring hygiene (something that is particularly important in relation to pandemics such as COVID-19), and ease of use. Prior work includes low-resolution (relatively unreliable) 3D analysis of wrinkles, or higher resolution ridge analysis that usually employs a commercial (contact based) palmprint scanner. This gap between low and high-resolution palmprint recognition is bridged here using high-resolution non-contact photometric stereo. A camera and illuminants are synchronised with image capture to recover high-definition 3D texture data from the palm, which are then analysed to extract ridges and wrinkles. This novel low cost approach can tolerate distortions inherent to unconstrained contactless palmprint acquisition. Features are found using discrete Fourier transforms. After alignment to a global ridge pattern, feature correspondences are matched, enabling reliable non-contact palmprint identification. The system was evaluated on a medium-sized database and matching was achieved with 0.1% equal error rate, which shows that the approach can achieve accurate and user-friendly palmprint recognition.

10.
11th International Conference on Information Systems and Advanced Technologies, ICISAT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1730951

ABSTRACT

Logarithmic Transformation of Local Binary Pattern (LT-LBP) is introduced with machine learning algorithms for the classification of covid CT images. Preprocessing the input information plays a key role in machine learning as well as deep learning models. CT images and Chest X-rays are significant in diagnosing the disease. Most of the medical images are greyscale images. Texture analysis is one of the ways to obtain information from medical images and Local binary pattern is an efficient texture operator. With the texture pattern LBP, a novel method as Logarithmic Transformation of Local Binary Pattern (LT-LBP) is proposed in this paper. We applied 2695 CT images and 115 Italian COVID Positive CT images and 5 COVID positive CT images from the Chennai region. The CT-Scans and Chest X-ray images have endured for preprocessing and texture analysis with Local Binary Pattern (LBP) and trained with Support Vector Machine (SVM), K-nearest neighbors (KNN), Random Forest (RF), Logistic Regression (LR) as machine learning algorithms. The LT-LBP gives a better result when compared with normal LBP when combined with SVM and RF. The retrospective study gives the result as accuracy percentage of 95.7 with LT-LBP combined with SVM and also 91.4 percent of accuracy results for LT-LBP with RF © 2021 IEEE.

11.
2nd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2021 ; : 24-28, 2021.
Article in English | Scopus | ID: covidwho-1708938

ABSTRACT

Lung ultrasound can potentially diagnose lung abnormalities such as pneumonia and covid-19, but it requires high experience. Covid-19, as a global pandemic, has similar common symptoms as pneumonia. The proper diagnosis of covid-19 and pneumonia necessitates clinicians' high expertise and skill to classify Covid-19 disease. This paper presents an approach to differentiate pneumonia and covid-19 based on texture analysis of ultrasound images. The proposed scheme is based on the Gray Level Co-occurrence Matrix (GLCM) features computing with Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma transformation for image enhancement. The results of the feature extraction analysis for lung ultrasound images suggest that differentiating pneumonia and Covid-19 is possible based on image texture features. © 2021 IEEE.

12.
Eur Radiol ; 32(6): 4314-4323, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1637024

ABSTRACT

INTRODUCTION: Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) software has already been widely used in the evaluation of interstitial lung diseases (ILD) but has not yet been tested in patients affected by COVID-19. Our aim was to use it to describe the relationship between Coronavirus Disease 2019 (COVID-19) outcome and the CALIPER-detected pulmonary vascular-related structures (VRS). MATERIALS AND METHODS: We performed a multicentric retrospective study enrolling 570 COVID-19 patients who performed a chest CT in emergency settings in two different institutions. Fifty-three age- and sex-matched healthy controls were also identified. Chest CTs were analyzed with CALIPER identifying the percentage of VRS over the total lung parenchyma. Patients were followed for up to 72 days recording mortality and required intensity of care. RESULTS: There was a statistically significant difference in VRS between COVID-19-positive patients and controls (median (iqr) 4.05 (3.74) and 1.57 (0.40) respectively, p = 0.0001). VRS showed an increasing trend with the severity of care, p < 0.0001. The univariate Cox regression model showed that VRS increase is a risk factor for mortality (HR 1.17, p < 0.0001). The multivariate analysis demonstrated that VRS is an independent explanatory factor of mortality along with age (HR 1.13, p < 0.0001). CONCLUSION: Our study suggests that VRS increases with the required intensity of care, and it is an independent explanatory factor for mortality. KEY POINTS: • The percentage of vascular-related structure volume (VRS) in the lung is significatively increased in COVID-19 patients. • VRS showed an increasing trend with the required intensity of care, test for trend p< 0.0001. • Univariate and multivariate Cox models showed that VRS is a significant and independent explanatory factor of mortality.


Subject(s)
COVID-19 , Humans , Informatics , Lung/diagnostic imaging , Retrospective Studies , Software
13.
Radiol Med ; 126(11): 1415-1424, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1340481

ABSTRACT

PURPOSE: To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. MATERIALS AND METHODS: One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann-Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. RESULTS: Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). CONCLUSIONS: Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.


Subject(s)
COVID-19/diagnostic imaging , Lung Diseases, Interstitial/diagnostic imaging , Tomography, X-Ray Computed , Adolescent , Adult , Aged , Aged, 80 and over , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pilot Projects , Retrospective Studies , Young Adult
14.
Pattern Recognit ; 119: 108083, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1253453

ABSTRACT

COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As of April 2021, the number of confirmed cases of COVID-19 reported to the World Health Organization has already exceeded 135 million worldwide, while the number of deaths exceeds 2.9 million. Due to the impacts of the disease, efforts in the literature have intensified in terms of studying approaches aiming to detect COVID-19, with a focus on supporting and facilitating the process of disease diagnosis. This work proposes the application of texture descriptors based on phylogenetic relationships between species to characterize segmented CT volumes, and the subsequent classification of regions into COVID-19, solid lesion or healthy tissue. To evaluate our method, we use images from three different datasets. The results are promising, with an accuracy of 99.93%, a recall of 99.93%, a precision of 99.93%, an F1-score of 99.93%, and an AUC of 0.997. We present a robust, simple, and efficient method that can be easily applied to 2D and/or 3D images without limitations on their dimensionality.

15.
Biomed Signal Process Control ; 68: 102582, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1163450

ABSTRACT

Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 % , a sensitivity of 96.1 % , and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.

16.
Eur J Radiol ; 137: 109602, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1084604

ABSTRACT

PURPOSE: Differentiating COVID-19 from other acute infectious pneumonias rapidly is challenging at present. This study aims to improve the diagnosis of COVID-19 using computed tomography (CT). METHOD: COVID-19 was confirmed mainly by virus nucleic acid testing and epidemiological history according to WHO interim guidance, while other infectious pneumonias were diagnosed by antigen testing. The texture features were extracted from CT images by two radiologists with 5 years of work experience using modified wavelet transform and matrix computation analyses. The random forest (RF) classifier was applied to identify COVID-19 patients and images. RESULTS: We retrospectively analysed the data of 95 individuals (291 images) with COVID-19 and 96 individuals (279 images) with other acute infectious pneumonias, including 50 individuals (160 images) with influenza A/B. In total, 6 texture features showed a positive association with COVID-19, while 4 features were negatively associated. The mean AUROC, accuracy, sensitivity, and specificity values of the 5-fold test sets were 0.800, 0.722, 0.770, and 0.680 for image classification and 0.858, 0.826, 0.809, and 0.842 for individual classification, respectively. The feature 'Correlation' contributed most both at the image level and individual level, even compared with the clinical factors. In addition, the texture features could discriminate COVID-19 from influenza A/B, with an AUROC of 0.883 for images and 0.957 for individuals. CONCLUSIONS: The developed texture feature-based RF classifier could assist in the diagnosis of COVID-19, which could be a rapid screening tool in the era of pandemic.


Subject(s)
COVID-19 , Humans , Machine Learning , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
17.
Turk J Med Sci ; 51(3): 972-980, 2021 06 28.
Article in English | MEDLINE | ID: covidwho-1067814

ABSTRACT

Background/aim: To investigate the changes in the spleen size, parenchymal heterogeneity, and computed tomography (CT) texture analysis features of patients diagnosed with Coronavirus disease 2019 (COVID-19) Materials and methods: The size and parenchymal structure of the spleen in 91 patients who underwent thoracic CT examination due to COVID-19 were evaluated. For the evaluation of parenchymal heterogeneity, CT texture analysis was performed using dedicated software (Olea Medical, France). The texture analysis of each case consisted of 15 first-order intensity-based features, 17 gray level co- occurrence matrix-based features, and 9 gray level run length matrix-based features. Results: A total of 91 patients (45 males, 46 females) with a mean age of 54.31 ± 16.33 years (range: 18­81) were included in the study. A statistically significant decrease in spleen size was seen in the follow-up CT examinations (p < 0.001) whereas no statistically significant difference was found between the Hounsfield unit (HU) values. The radiomics consisted of first-order intensity-based features such as 90th percentile, maximum, interquartile range, range, mean absolute deviation, standard deviation, and variance, all of which showed statistically significant differences (p-values: < 0.001, < 0.001, 0.001, 0.003, 0.001, 0.001, and 0.004, respectively). "Correlation" as a gray level co-occurrence matrix-based feature and "gray level nonuniformity" as a gray level run length matrix-based feature showed statistically differences (p-values: 0.033 and < 0.001, respectively). Conclusions: Although COVID-19 manifests with lung involvement in the early stage, it can also cause systemic involvement, and the spleen may be one of its target organs. A decrease in the spleen size and parenchymal microstructure changes can be observed in the short follow-up time. It is hoped that the changes in the parenchymal microstructure will be demonstrated by a noninvasive method: texture analysis.


Subject(s)
COVID-19/diagnosis , SARS-CoV-2 , Spleen/diagnostic imaging , Splenic Diseases/epidemiology , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , Comorbidity , Female , Follow-Up Studies , Humans , Male , Middle Aged , Pandemics , Retrospective Studies , Splenic Diseases/diagnosis , Turkey/epidemiology , Young Adult
18.
Multimed Tools Appl ; 80(4): 5423-5447, 2021.
Article in English | MEDLINE | ID: covidwho-1060388

ABSTRACT

The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, 1.396 lung CT images in total (386 Covid-19 and 1.010 Non-Covid-19) were subjected to automatic classification. In this study, Convolutional Neural Network (CNN), one of the deep learning methods, was used which suggested automatic classification of CT images of lungs for early diagnosis of Covid-19 disease. In addition, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) was used to compare the classification successes of deep learning with machine learning. Within the scope of the study, a 23-layer CNN architecture was designed and used as a classifier. Also, training and testing processes were performed for Alexnet and Mobilenetv2 CNN architectures as well. The classification results were also calculated for the case of increasing the number of images used in training for the first 23-layer CNN architecture by 5, 10, and 20 times using data augmentation methods. To reveal the effect of the change in the number of images in the training and test clusters on the results, two different training and testing processes, 2-fold and 10-fold cross-validation, were performed and the results of the study were calculated. As a result, thanks to these detailed calculations performed within the scope of the study, a comprehensive comparison of the success of the texture analysis method, machine learning, and deep learning methods in Covid-19 classification from CT images was made. The highest mean sensitivity, specificity, accuracy, F-1 score, and AUC values obtained as a result of the study were 0,9197, 0,9891, 0,9473, 0,9058, 0,9888; respectively for 2-fold cross-validation, and they were 0,9404, 0,9901, 0,9599, 0,9284, 0,9903; respectively for 10-fold cross-validation.

19.
Curr Med Imaging ; 17(9): 1094-1102, 2021.
Article in English | MEDLINE | ID: covidwho-1028188

ABSTRACT

BACKGROUND: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. OBJECTIVE: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. METHODS: Chest X-ray images were accessed from a publicly available repository(https://www.kaggle. com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal region of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. RESULTS: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensembled. According to Youden's index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. CONCLUSION: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients.


Subject(s)
COVID-19 , Humans , Proof of Concept Study , SARS-CoV-2 , Tomography, X-Ray Computed , X-Rays
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