scholarly journals Covid-19 Detection using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3835-3540
Author(s):  
M. Srilekha Reddy
Keyword(s):  
Global Economy ◽  
Transmission Rate ◽  
False Negative ◽  
Image Features ◽  
Diagnostic Methods ◽  
Classification Model ◽  
Support Vector ◽  
Classification Rate ◽  
X Ray ◽  
Chest X Ray

Recently, the virus (COVID-19) has spread widely throughout the world and has led to the examination of large numbers of suspected cases using standard COVID-19 tests and has become pandemic. Everyday life, public health and the global economy have been destroyed. The pathogenic laboratory tests such as Polymerase chain reaction (PCR) take a long time with false negative results and are considered the gold standard for diagnosis. Therefore, there was an urgent need for rapid and accurate diagnostic methods to detect COVID-19 cases as soon as possible to prevent the spread of this epidemic and combat it. Applying advanced artificial intelligence techniques along with radiography may be helpful in detecting this disease. In this study, we propose a classification model that detect the infected condition through the chest X-ray images. A dataset containing chest x-ray images of normal people, people with pneumonia such as SARS, streptococcus and pneumococcus and other patients with COVID- 19 were collected. Histogram of oriented gradients (HOG) is used for image features extraction. The images are then classified using Support Vector Machines (SVM), random forests and K- nearest neighbours (KNN), with classification rate 98.14%, 96.29% and 88.89% respectively. These results may contribute efficiently in detecting COVID-19 disease. The input dataset is taken from Kaggle which provides the dataset to analyse and helps to get the best possible solutions from the set of problems. Kaggle is launching a companion COVID-19 forecasting challenges to help answer a subset of the NASEM/WHO questions. While the challenge involves forecasting confirmed cases and fatalities between April 1 and April 30 by region, the primary goal isn't only to produce accurate forecasts. It’s also to identify factors that appear to impact the transmission rate of COVID-19.

scholarly journals COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function

2021 ◽  
Vol 21 (1) ◽  
pp. 31-42
Author(s):  
I Gusti Ayu Agung Diatri Indradewi ◽  
Ni Wayan Sumartini Saraswati ◽  
NI Wayan Wardani
Keyword(s):  
Feature Extraction ◽  
Error Rate ◽  
Haar Wavelet ◽  
Quality Parameters ◽  
Classification Model ◽  
Support Vector ◽  
Daubechies Wavelet ◽  
X Ray ◽  
Chest X Ray ◽  
Parameters Of Accuracy

Our previous work regarding the X-Ray detection of COVID-19 using Haar wavelet feature extraction and the Support Vector Machines (SVM) classification machine has shown that the combination of the two methods can detect COVID-19 well but then the question arises whether the Haar wavelet is the best wavelet method. So that in this study we conducted experiments on several wavelet methods such as biorthogonal, coiflet, Daubechies, haar, and symlets for chest X-Ray feature extraction with the same dataset. The results of the feature extraction are then classified using SVM and measure the quality of the classification model with parameters of accuracy, error rate, recall, specification, and precision. The results showed that the Daubechies wavelet gave the best performance for all classification quality parameters. The Daubechies wavelet transformation gave 95.47% accuracy, 4.53% error rate, 98.75% recall, 92.19% specificity, and 93.45% precision.

Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images v1

2021 ◽  
Author(s):  
Thanakorn Poomkur ◽  
Thakerng Wongsirichot
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Computational Time ◽  
Support Vector ◽  
X Ray ◽  
Learning Techniques ◽  
Hybrid Classification ◽  
Chest X Ray ◽  
Segmentation Image

The coronavirus disease of 2019 (COVID-19) has been declared a pandemic and has raised worldwide concern. Lung inflammation and respiratory failure are commonly observed in moderate-to-severe cases. Chest X-ray imaging is compulsory for diagnosis, and interpretation is commonly performed by skilled medical specialists. Many studies have been conducted using machine learning approaches such as Deep Learning (DL) with acceptable accuracy. However, other dimensions such as computational time were less discussed. Thus, our work is motivated to design anew computer-aided diagnosis (CADx) tool for identifying chest X-ray images of COVID-19 infection using machine learning techniques including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs). Our work is designed with the concept of multi-layer classification architecture and performs with minimal computational time and acceptable classification results. First, image segmentation, image enhancement and feature extraction techniques are performed. Second, machine learning techniques are selected based on classification performance. Finally, selected machine learning techniques are assembled into a multi-layer hybrid classification model for COVID-19 (MLHC-COVID-19). Specifically, the MLHC-COVID-19 consists of two layers, Layer I: Healthy and Unhealthy; Layer II: COVID-19 and non-COVID-19.

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

Przewlekły kaszel jako główna manifestacja chłoniaka Hodgkina – opis przypadku

2019 ◽  
Vol 23 (3) ◽  
Author(s):  
Katarzyna Wójcicka ◽  
Andrzej Pogorzelski
Keyword(s):  
Chronic Cough ◽  
Correct Diagnosis ◽  
Diagnostic Methods ◽  
Radiographic Examination ◽  
Older Children ◽  
X Ray ◽  
Volume Curve ◽  
Flow Volume Curve ◽  
Mediastinal Widening ◽  
Chest X Ray

A cough lasting longer than 4-8 weeks, defined as chronic cough, always requires thorough diagnostic evaluation. In addition to detailed history-taking and physical examination, simple and available diagnostic methods, such as chest x-ray and spirometry, should be performed. They may be helpful tool to establish the underlying cause of cough. Many younger children may have difficulties in performing the forced expiratory maneuvers and fulfilling repeatability criteria for spirometry. The disturbances resulting from insufficient cooperation should be considered in interpratation of the obtained results. The shape of the flow-volume curve, which suggests upper or central airways obstruction, can not be ignored and always requires further investigation for diagnosis of respiratory pathology. The chest x-ray is the most frequently performed radiographic examination in children. Accurate interpretation is essential in reaching a correct diagnosis. Mediastinal widening on the chest x-ray in children can occur due to a large variety of causes. The normal thymus can take on a variety of sizes and shapes and still be considered normal in the first few years of life. In older children mediastinal widening should be differentiated from mediastinal masses. Lymph node enlargement represents a frequent cause, usually as a result of infection or malignancy. The article reports a case of a 12-year-old boy with chronic cough, mediastinal widening on the chest X-ray and abnormal spirometry results, who was finally diagnosed with stage III Hodgkin’s lymphoma.

scholarly journals Detection of Fracture Bones in X-ray Images Categorization

2020 ◽  
pp. 1-11
Author(s):  
Adigun Oyeranmi ◽  
Babatunde Ronke ◽  
Rufai Mohammed ◽  
Aigbokhan Edwin
Keyword(s):  
Feature Extraction ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
False Negative ◽  
Support Vector ◽  
Kappa Statistics ◽  
Fracture Detection ◽  
True Negative ◽  
Research Attention ◽  
X Ray

Fractured bone detection and categorization is currently receiving research attention in computer aided diagnosis system because of the ease it has brought to doctors in classification and interpretation of X-ray images.  The choice of an efficient algorithm or combination of algorithms is paramount to accurately detect and categorize fractures in X-ray images, which is the first stage of diagnosis in treatment and correction of damaged bones for patients. This is what this research seeks to address. The research design involves data collection, preprocessing, segmentation, feature extraction, classification and evaluation of the proposed method. The sample dataset were x-ray images collected from the Department of Radiology, National Orthopedic Hospital, Igbobi-Lagos, Nigeria as well as Open Access Medical Image Repositories. The image preprocessing involves the conversion of images in RGB format to grayscale, sharpening and smoothing using Unsharp Masking Tool.  The segmentation of the preprocessed image was carried out by adopting the Entropy method in the first stage and Canny edge method in the second stage while feature extraction was performed using Hough Transformation. Detection and classification of fracture image employed a combination of two algorithms;  K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) for detecting fracture locations based on four classification types: (normal, comminute, oblique and transverse).Two performance assessment methods were employed to evaluate the developed system. The first evaluation was based on confusion matrix which evaluates fracture and non-fracture on the basis of TP (True Positive), TN (True negative), FP (False Positive) and FN (False Negative). The second appraisal was based on Kappa Statistics which evaluates the type of fracture by determining the accuracy of the categorized fracture bone type. The result of first assessment for fracture detection shows that 26 out of 40 preprocessed images were fractured, resulting to the following three values of performance metrics: accuracy value of 90%, sensitivity of 87% and specificity of 100%. The Kappa coefficient error assessment produced accuracy of 83% during classification. The proposed method can find suitable use in categorization of fracture types on different bone images based on the results obtained from the experiment.

Can We Screen Febrile Children for Pneumonia and/or Bacteremia Without Chest X-ray Films and Blood Cultures?

PEDIATRICS ◽  
1978 ◽  
Vol 61 (2) ◽  
pp. 332-333
Author(s):  
Henry M. Feder
Keyword(s):  
Young Children ◽  
Prospective Study ◽  
False Negative ◽  
Blood Cultures ◽  
Chest Roentgenogram ◽  
Negative Group ◽  
X Ray ◽  
Chest X Ray ◽  
Wbc Count ◽  
A Prospective Study

McCarthy et al. in their article "Temperature Greater Than or Equal to 40 C in Children Less Than 24 Months of Age: A Prospective Study" (Pediatrics 59:663, May 1977) recommend using both WBC count (≥ 15,000/cu mm) and ESR (≥ 30 mm/hr) for screening febrile young children for pneumonia or bacteremia. If either is elevated they suggest doing blood cultures and taking a chest roentgenogram. However, in 25% of their patients with bacteremia and 42% of their patients with pneumonia neither WBC count nor ESR was elevated, leaving a sizable false-negative group.

scholarly journals A gyermekkori alsó légúti idegentest-aspiráció diagnosztikája a klinikai gyakorlatban

2018 ◽  
Vol 159 (51) ◽  
pp. 2162-2166
Author(s):  
Dániel Hajnal ◽  
Tamás Kovács
Keyword(s):  
Foreign Body ◽  
Standard Procedure ◽  
False Negative ◽  
Radiological Finding ◽  
Paediatric Population ◽  
Rigid Bronchoscopy ◽  
Foreign Body Aspiration ◽  
X Ray ◽  
Co Morbidity ◽  
Chest X Ray

Abstract: Introduction and aim: Rigid bronchoscopic foreign body removal is the gold standard procedure for foreign body aspiration. We have analysed our results of bronchoscopies and the accuracy of diagnosis among the paediatric population in Southeast Hungary. Method: A retrospective study of children admitted because of suspected solid foreign body aspiration between 2006 and 2017 was performed. Results: From among 220 admitted patients, 86 were suspected of solid particle aspiration. Presenting history was certain in 68.6% (n = 59/86). Sudden choking-like symptoms were present in 61/86 patients (70.9%), coughing in 81/86 patients (94.2%). Thoracic auscultation was positive in 67/86 cases (77.9%), chest X-ray in 75/86 patients (87.2%), while fluoroscopy only in 12/75 cases (16%). 92 bronchoscopies in 86 patients were performed. In 57 bronchoscopies, solid foreign body was found (66.2%) and the removal was successful in 56 cases. Thoracic auscultation was negative in patients with foreign body only in 6/57 cases (10.5%). In the same group, chest X-ray was negative in 33/57 cases (57.9%) and fluoroscopy was positive only in 12/57 patients (21.1%). Pneumonia or prolonged bronchitis was present in 4/86 patients (4.6%). Severe bronchial bleeding occurred in 2/86 cases (2.3%). Mortality was 1.2%, a child with severe co-morbidity and chronic aspiration passed away. Bronchoscopy was negative in 29/86 patients (33.7%). Complications were significantly higher in chronic cases than in the acute ones. Conclusion: Rigid bronchoscopy is indicated if solid foreign body aspiration is suspected and positive anamnesis, typical symptoms (coughing, choking) or positive chest auscultations are present. Diagnosis predominantly based on radiological finding is controversial due to the high possibility of false negative results. Early intervention within the first 24 hours is recommended to avoid complications. Orv Hetil. 2018; 159(51): 2162–2166.

Comparison of Baseline, Bone-Subtracted, and Enhanced Chest Radiographs for Detection of Pneumothorax

2020 ◽  
pp. 084653712090885
Author(s):  
Fatemeh Homayounieh ◽  
Subba R. Digumarthy ◽  
Jennifer A. Febbo ◽  
Sherief Garrana ◽  
Chayanin Nitiwarangkul ◽  
...  
Keyword(s):  
False Negative ◽  
Tension Pneumothorax ◽  
Area Under The Curve ◽  
Ground Truth ◽  
Chest Radiographs ◽  
Detection Rates ◽  
X Ray ◽  
Institutional Review ◽  
Type Area ◽  
Chest X Ray

Purpose: To assess and compare detectability of pneumothorax on unprocessed baseline, single-energy, bone-subtracted, and enhanced frontal chest radiographs (chest X-ray, CXR). Method and Materials: Our retrospective institutional review board–approved study included 202 patients (mean age 53 ± 24 years; 132 men, 70 women) who underwent frontal CXR and had trace, moderate, large, or tension pneumothorax. All patients (except those with tension pneumothorax) had concurrent chest computed tomography (CT). Two radiologists reviewed the CXR and chest CT for pneumothorax on baseline CXR (ground truth). All baseline CXR were processed to generate bone-subtracted and enhanced images (ClearRead X-ray). Four radiologists (R1-R4) assessed the baseline, bone-subtracted, and enhanced images and recorded the presence of pneumothorax (side, size, and confidence for detection) for each image type. Area under the curve (AUC) was calculated with receiver operating characteristic analyses to determine the accuracy of pneumothorax detection. Results: Bone-subtracted images (AUC: 0.89-0.97) had the lowest accuracy for detection of pneumothorax compared to the baseline (AUC: 0.94-0.97) and enhanced (AUC: 0.96-0.99) radiographs ( P < .01). Most false-positive and false-negative pneumothoraces were detected on the bone-subtracted images and the least numbers on the enhanced radiographs. Highest detection rates and confidence were noted for the enhanced images (empiric AUC for R1-R4 0.96-0.99). Conclusion: Enhanced CXRs are superior to bone-subtracted and unprocessed radiographs for detection of pneumothorax. Clinical Relevance/Application: Enhanced CXRs improve detection of pneumothorax over unprocessed images; bone-subtracted images must be cautiously reviewed to avoid false negatives.

The intracavitary electrocardiography method for positioning the tip of epicutaneous cava catheter in neonates: Pilot study

2018 ◽  
Vol 19 (6) ◽  
pp. 542-547 ◽  
Author(s):  
Antonella Capasso ◽  
Rossella Mastroianni ◽  
Annalisa Passariello ◽  
Marta Palma ◽  
Francesco Messina ◽  
...  
Keyword(s):  
Pilot Study ◽  
Superior Vena ◽  
False Negative ◽  
Vena Cava ◽  
P Wave ◽  
Target Zone ◽  
X Ray ◽  
Catheter Tip ◽  
Chest X Ray ◽  
Head Neck

Purpose: The neonatologists of Sant’Anna and San Sebastiano Hospital of Caserta have carried out a pilot study investigating the safety, feasibility, and accuracy of intracavitary electrocardiography for neonatal epicutaneous cava catheter tip positioning. Patients and methods: We enrolled 39 neonates (1–28 days of postnatal age or correct age lower than 41 weeks) requiring epicutaneous cava catheter in the district of superior vena cava (head–neck or upper limbs). Intracavitary electrocardiography was applicable in 38 neonates. Results: No significant complications related to intracavitary electrocardiography occurred in the studied neonates. The increase in P wave on intracavitary electrocardiography was detected in 30 cases. Of the remaining eight cases, six malpositioned catheters tipped out of cavoatrial junction–target zone (chest x-ray and echocardiographical control) and two were false negative (tip located in target zone). The match between intracavitary electrocardiography and x-ray was observed in 29/38 cases, and the same ratio between intracavitary electrocardiography and echocardiography was detected. Conclusion: We conclude that the intracavitary electrocardiography method is safe and accurate in neonates as demonstrated in pediatric and adult patients. The applicability of the method is 97% and its feasibility is 79%. The overall accuracy is 76% but it rises to 97% if “peak” P wave is detected.

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