Comparison of efficacy of lung ultrasound and chest X-ray in diagnosing pulmonary edema and pleural effusion in ICU patients: A single centre, prospective, observational study

Background and Aims While chest X-ray (CXR) has been a conventional tool in intensive care units (ICUs) to identify lung pathologies, computed tomography (CT) scan remains the gold standard. Use of lung ultrasound (LUS) in resource-rich ICUs is still under investigation. The present study compares the utility of LUS to that of CXR in identifying pulmonary edema and pleural effusion in ICU patients. In addition, consolidation and pneumothorax were analyzed as secondary outcome measures. Material and Methods This is a prospective, single centric, observational study. Patients admitted in ICU were examined for lung pathologies, using LUS by a trained intensivist;and CXR done within 4 hours of each other. The final diagnosis was ascertained by an independent senior radiologist, based on the complete medical chart including clinical findings and the results of thoracic CT, if available. The results were compared and analyzed. Results Sensitivity, specificity and diagnostic accuracy of LUS was 95%, 94.4%, 94.67% for pleural effusion;and 98.33%, 97.78%, 98.00% for pulmonary edema respectively. Corresponding values with CXR were 48.33%, 76.67%, 65.33% for pleural effusion;and 36.67%, 82.22% and 64.00% for pulmonary edema respectively. Sensitivity, specificity and diagnostic accuracy of LUS was 91.30%, 96.85%, 96.00% for consolidation;and 100.00%, 79.02%, 80.00% for pneumothorax respectively. Corresponding values with CXR were 60.87%, 81.10%, 78.00% for consolidation;and 71.3%, 97.20%, 96.00% for pneumothorax respectively. Conclusion LUS has better diagnostic accuracy in diagnosis of pleural effusion and pulmonary edema when compared with CXR and is thus recommended as an effective alternative for diagnosis of these conditions in acute care settings. Our study recommends that a thoracic CT scan can be avoided in most of such cases.

A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.

Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images

Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system’s performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.

The Role of Chest X-Ray in Monitoring Lung Changes among COVID-19 Patients in Gaza Strip

Objective: To investigate the time course and findings severity of COVID-19 infection at chest radiography based on a 6-point radiological severity score, and correlates these with patients’ age and gender. Methods: This is a retrospective study of COVID-19 patients who were admitted at European Gaza Hospital and evaluated between October 6, 2020, and November 30, 2020. Baseline and serial chest radiographs, up to 4 images per patient, were reviewed and assessed for predominant pattern, side, and location of lung opacity. Utilized a 6-point scoring system, which divides the chest X-ray into 6 zones, to assess chest X-ray changes and correlate them with the severity of infection, age, and gender of patients. Results: The study included 136 COVID-19 patients: (51/136, 37%) were males and (85/136, 62.5%) were females, while age ranged from 7 months to 90 years with a mean age of 41.7 ± (19.5) years. Negative Chest x-rays were more observed than positive images. Ground-glass opacity was the most frequent pattern with a decreasing trend from 1st to 4th chest X-ray (from 33.8% to 3.7%), followed by consolidation (from 16.2% to 2.9%). Also, the commonest pattern of opacity was seen in peripheral areas (27/136, 19.9%), lower zone location (23/136, 16.9%), and bilateral opacity involvement (43/136;31.6%). No significant correlation was noticed between the patient’s gender, age, and severity score (P > 0.05). Conclusions: The 6-point chest X-ray severity score as a predictive tool in assessing the severity due to provide an assessment of the progression or regression pathway.

Diagnostic Value of the Thoracic Ultrasonography Compared to Conventional Chest X-Rays in Pneumonia for Children between 0 to 15 Years: Case Study in Two Hospitals in Yaoundé

Introduction: The diagnosis of pneumonia is usually made based on clinical manifestations and chest X-ray. The use of ultrasound in detecting pulmonary diseases in general, and especially consolidation syndrome has been demonstrated. The objective of this study was to determine the accuracy of thoracic ultrasound compared to chest X-ray in the diagnosis of infectious pneumonia in children. Methods: Children between 0 to 15 years were included in our study. The lung ultrasound results obtained were compared with those of the chest X-ray used as the reference. Our data were introduced into the EpiInfo 3.5.4 software and analyzed with the EpiInfo 3.5.4 and IBMSPSS Statistics version 20.0 softwares. Microsoft Office Excel 2016 was used to produce Charts. Continuous quantitative variables were presented. Cohen’s Kappa concordance test was applied with confidence interval of 95%. Results: 52 children were enrolled in the study. In imaging, the dominant sign was consolidation syndrome (75.0%) of cases by chest radiography, and in 78.8% of cases by lung ultrasound (p Conclusion: Our study demonstrated that lung echography is a non-ionizing and reliable tool in the diagnosis of childhood’s pneumonia.

Automatic Detection of COVID-19 Infection Using Chest X-Ray Images Through Transfer Learning

The new coronavirus(COVID-19),declared by the World Health Organization as a pandemic,has infected more than 1 million people and killed more than 50 thousand.An infection caused by COVID-19 can develop into pneumonia,which can be detected by a chest X-ray exam and should be treated appropriately.In this work,we propose an automatic detection method for COVID-19 infection based on chest X-ray images.The datasets constructed for this study are composed of194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients.Since few images of patients with COVID-19 are publicly available,we apply the concept of transfer learning for this task.We use different architectures of convolutional neural networks(CNNs)trained on Image Net,and adapt them to behave as feature extractors for the X-ray images.Then,the CNNs are combined with consolidated machine learning methods,such as k-Nearest Neighbor,Bayes,Random Forest,multilayer perceptron(MLP),and support vector machine(SVM).The results show that,for one of the datasets,the extractor-classifier pair with the best performance is the Mobile Net architecture with the SVM classifier using a linear kernel,which achieves an accuracy and an F1-score of 98.5%.For the other dataset,the best pair is Dense Net201 with MLP,achieving an accuracy and an F1-score of 95.6%.Thus,the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images.

Impact of Standardized Scheme on the Detection of Chest X-Ray Abnormalities and Radiographic Diagnosis of Pulmonary Tuberculosis in Adult

Purpose: The complexity of chest radiography (CXR) is a source of variability in its interpretation. We assessed the effect of an interpretation grid on the detection of CXR anomalies and radio- graphic diagnosis of tuberculosis in an endemic area for tuberculosis. Methods: The study was conducted in Yaounde (Cameroon). Six observers (2 pulmonologists, 2 radiologists and 2 senior residents in medical imaging) interpreted 47 frontal CXR twice two months apart without (R1) and with (R2) the aid of an interpretation grid. We focused on the detection of micro nodules (n = 16), cavitations (n = 12), pleural effusion (n = 6), adenomegaly (n = 6), and diagnosis of tuberculosis (n = 23) and cancer (n = 7). Results: The average score for accurate detection of elementary lesions was 40.4% [95%CI: 25% - 58.3%] in R1 and 52.1% [36.9% - 65.3%] in R2. The highest im- provement was observed for micro nodules (19.8%). Cavitations had the highest proportions of accurate detections (58.3% in R1 and 65.3% in R2). The average score of accurate diagnosis was 46.1% in R1 and 57.4% in R2. Accurate diagnosis improved by 3.6% for tuberculosis and 19% for cancer between R1 and R2. Intra-observer agreement was higher for the diagnosis of cancers (0.22 ≤ k ≤ 1) than for diagnosing tuberculosis (0.21 ≤ k ≤ 0.68). Inter-observer agreement was highly variable with a modest improvement for the diagnosis of tuberculosis in R2. Conclusion: Standardized interpretation scheme improved the detection of CXR anomalies and diagnosis of tuberculosis. It significantly improved inter-observer’s agreement in diagnosing tuberculosis but not in detecting most lesions.

Inter-Observer Variability in the Detection and Interpretation of Chest X-Ray Anomalies in Adults in an Endemic Tuberculosis Area

Purpose: To assess the inter-observer agreement in reading adults chest radiographs (CXR) and determine the effectiveness of observers in radiographic diagnosis of pulmonary tuberculosis (PTB) in a tuberculosis endemic area. Methods: A quasi-observational study was conducted in the Pneumology Department of Yaounde Jamot Hospital (Cameroon) from January to March 2014. This included six observers (two chest physicians, two radiologists, two end-training residents in medical imaging) and 47 frontal CXRs (4 of diffuse interstitial lung disease, 6 normal, 7 of lung cancers, 7 of bacterial pneumonia, 23 of PTB). The sample size was calculated on the basis of an expected 0.47 Kappa with a spread of 0.13 (α = 5%, CI = 95%) for six observers and five diagnostic items. The analysis of concordance was focused on the detection of nodules, cavitary lesions, pleural effusion, adenomegaly and diagnosis of PTB and lung cancer. These intervals of kappa coefficient were considered: discordance (0.81). Results: The average score for the detection of caverns was the highest (58.3%) followed by that of the correct diagnosis of tuberculosis (49.3%). Pneumologists had the highest proportions of correct diagnosis of tuberculosis (69.6% and 73.9%) and better inter-observer agreement (k = 0.71) for PTB diagnosis. Observers were more in agreement for the detection of nodules (0.32 - 0.74), adenomegalies (0.43 - 0.69), and for the diagnosis of cancer (0.22 - 1) than for the diagnosis of tuberculosis (0.19 - 0.71). Disagreements were more frequent for the detection of pleural effusions (-0.08 - 0.73). Conclusion: The inter-observer agreement varies with the type of lesions and diagnosis. Pneumologists were most effective for the diagnosis of pulmonary tuberculosis. Observers were more in agreement for the detection of nodules and the diagnosis of cancer than for the diagnosis of pulmonary tuberculosis.

Pertinence of Children’s Chest X-Ray Request Form and Practice at the Regional Hospital of Ngaoundere Cameroon

Background: Chest X-ray is frequently performed for evaluation of chest disease in both adults and children. Children are more exposed to the adverse effects of radiation as compared to adults. During our daily practice, we noticed that most of children’s chest X-ray results were normal. Purpose: This study aimed to evaluate the indications, the technic, the irradiation and the result of chest X-rays in children in order to know if the practice of these X-rays was relevant. Method: Cross-sectional and descriptive study conducted at the Imaging Regional Center of Ngaoundere from April to August 2017. A total number of 145 radiographs and 140 X-ray requests of 140 children were considered in this work. The conformity of the request were verified according to the recommendations of the National Agency for Accreditation and Health Evaluation in France (NAAHE), technical condition of realization and results were appreciated and the entrance surface dose (ESD) of the patients was estimated using a mathematical algorithm. Results: Children under 5 years (63.5%) were more represented in our study. The main indications were: cough (22.1%), suspicion of pneumonia (16.4%) and bronchitis (15.7%). No indication was mentioned on 69.3% of the request forms. After confrontation to the “Guide for proper use of medical imaging examinations” (GPU), we only had 24% conformity of indications. 82.7% of the examinations required immobilization assistance by the parents. Most of the children were imaged in a standing-up position (82.9%) and the anterior-posterior view (77.9%) was more practiced. After the analysis of the pictures, 62% of them presented an optimal contrast, while 42.1% of X-ray were performed without beam collimation. 25 X-rays were repeated: 12 (48%) because of patient’s motion and 13 (52%) of mispositionning. After interpretation, 87 (62.14%) chest X-ray were normal. Main lesion observed were pneumonia (17.14%) followed by bronchopeumopathy (5.71%) and bronchitis (5%). The obtained ESD values were 0.11, 0.15 and 0.17 mGy respectively for the 0 - 1 year, 1 - 5 year and 5 - 10 year age groups;0.2 and 0.57 respectively for postero-anterior (PA) and lateral (LAT) view for the age group 10 - 15 years, which were slightly greater than the values in internationally published studies. Conclusion: The request for children chest X-ray is not relevant in terms of indication, technical conditions of realization and irradiation.

Chest X-rays in detecting injuries caused by blunt trauma

BACKGROUND:The appropriate sequence of different imagings and indications of thoracic computed tomography(TCT)in evaluating chest trauma have not yet been clarified at present.The current study was undertaken to determine the value of chest X-ray(CXR)in detecting chest injuries in patients with blunt trauma.METHODS:A total of 447 patients with blunt thoracic trauma who had been admitted to the emergency department(ED)in the period of 2009–2013 were retrospectively reviewed.The patients met inclusion criteria(age>8 years,blunt injury to the chest,hemodynamically stable,and neurologically intact)and underwent both TCT and upright CXR in the ED.Radiological imagings were re-interpreted after they were collected from the hospital database by two skilled radiologists.RESULTS:Of the 447 patients,309(69.1%)were male.The mean age of the 447 patients was 39.5±19.2(range 9 and 87 years).158(35.3%)patients were injured in motor vehicle accidents(MVA).CXR showed the highest sensitivity in detecting clavicle fractures[95%CI 78.3(63.6–89)]but the lowest in pneuomediastinum[95%CI 11.8(1.5–36.4)].The specificity of CXR was close to 100%in detecting a wide array of entities.CONCLUSION:CXR remains to be the first choice in hemodynamically unstable patients with blunt chest trauma.Moreover,stable patients with normal CXR are candidates who should undergo TCT if significant injury has not been ruled out.

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Coronavirus(COVID-19)epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide.This newly recognized virus is highly transmissible,and no clinically approved vaccine or antiviral medicine is currently available.Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus.Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup.Here,a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray(CX-R)images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation.First,Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images,respectively.Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused.Parallel architecture,which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people,was considered.The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%,sensitivity of 99.90%,specificity of 100%,precision of 100%,F1-score of 99.93%,MSE of 0.021%,and RMSE of 0.016%in a large-scale dataset.This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

VGG-CovidNet: Bi-Branched Dilated Convolutional Neural Network for Chest X-Ray-Based COVID-19 Predictions

The coronavirus disease 2019(COVID-19)pandemic has had a devastating impact on the health and welfare of the global population.A key measure to combat COVID-19 has been the effective screening of infected patients.A vital screening process is the chest radiograph.Initial studies have shown irregularities in the chest radiographs of COVID-19 patients.The use of the chest X-ray(CXR),a leading diagnostic technique,has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases.This study introduces a dilated bi-branched convoluted neural network(CNN)architecture,VGG-COVIDNet,to detect COVID-19 cases from CXR images.The front end of the VGG-COVIDNet consists of the first 10 layers of VGG-16,where the convolutional layers in these layers are reduced to two to minimize latency during the training phase.The last two branches of the proposed architecture consist of dilated convolutional layers to reduce the model’s computational complexity while retaining the feature maps’spatial information.The simulation results show that the proposed architecture is superior to all the state-of-the-art architecture in accuracy and sensitivity.The proposed architecture’s accuracy and sensitivity are 96.5%and 96%,respectively,for each infection type.

Chest Ultrasound versus Chest X-Ray in Children with Lower Respiratory Tract Infections

Background: Lower respiratory tract infections (LRTIs) are among the leading causes of morbidity and mortality among children during early childhood. Lung ultrasound is a relatively simple bedside method helping early diagnosis of these conditions. Purpose: To compare the findings of chest X-ray (CXR) and of lung ultrasound (LUS) among children with lower respiratory tract infections. Subjects and Methods: A comparative cross-sectional study that included 100 children aged 6 months to 6 years with lower respiratory tract infections and 100 healthy age- and sex-matched children (controls). Included children were recruited from Mansoura University children hospital during the period from September 2020 to June 2021. Detailed findings of chest X-ray and lung ultrasound were compared. Results: Among 100 patients with LRTIs, the diagnosis of pneumonia, bronchiolitis and bronchitis were reported in 41%, 36% and 23% of cases respectively. Lung ultrasound was more sensitive than chest X-ray for diagnosing pneumonia (97.6% for LUS vs. 92.7% for CXR) and bronchiolitis (91.7% for LUS vs. 77.8% for CXR). Conclusion: Lung ultrasound is a sensitive tool for the assessment of lower respiratory tract infections. Further steps for application and validation are recommended.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.