RESUMEN
The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.
RESUMEN
Aim To explore the impact of coronavirus-2019 disease (COVID-19) pandemic on emergency reper-fusion characteristics in patients with ST-segment elevation myocardial infarction (STEMI) from non-epicenter. Methods This was a retrospective study involved STEMI patients undergoing primary percutaneous coronary intervention (PPCI), who admitted to chest pain center in our hospital during the pandemic ( from January 23 to March 29 in 2020) and the same period in 2019, excluding the patients with COVID-19. Clinical characteristics and reperfusion parameters were compared between the two groups. Results A total of 64 STEMI patients undergoing PPCI were enrolled in our study, including 13 patients during the pandemic and 51 patients during the same period in 2019. No differences occurred in admission signs, GRACE scores, arrival periods, transferred patterns,the period from door to troponin,and the period from first medical contact to dual antiplatelet between the two groups ( P>0. 05). As compared with 2019, STEMI patients undergoing PPCI had an apparent reduction. Meanwhile, significant delays appeared in reperfusion parameters, in-cluding the period from symptom onset to first medical contact (10 h vs. 3. 0 h, P<0. 001), the period from first medical contact to electrocardiogram (6 min vs. 3 min, P<0. 001), the period from door to troponin (15 min vs. 12 min, P = 0. 048), the period from door to device (76 min vs. 62 min, P = 0. 017), the period from telephone to catheter activated (15 min vs. 5 min, P<0. 001) and the period from catheter arrival to device (52 min vs. 41 min, P = 0. 033). Conclusion Even in non-epicenter, the COVID-19 outbreak still delayed mechanical reperfusion significantly. © 2022, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.
RESUMEN
In 2020, the global spread of Coronavirus Disease 2019 exposed entire world to a severe health crisis. This has limited fast and accurate screening of suspected cases due to equipment shortages and and harsh testing environments. The current diagnosis of suspected cases has benefited greatly from the use of radiographic brain imaging, also including X-ray and scintigraphy, as a crucial addition to screening tests for new coronary pneumonia disease. However, it is impractical to gather enormous volumes of data quickly, which makes it difficult for depth models to be trained. To solve these problems, we obtained a new dataset by data augmentation Mixup method for the used chest CT slices. It uses lung infection segmentation (Inf-Net [1]) in a deep network and adds a learning framework with semi-supervised to form a Mixup-Inf-Net semi-supervised learning framework model to identify COVID-19 infection area from chest CT slices. The system depends primarily on unlabeled data and merely a minimal amount of annotated data is required;therefore, the unlabeled data generated by Mixup provides good assistance. Our framework can be used to improve improve learning and performance. The SemiSeg dataset and the actual 3D CT images that we produced are used in a variety of tests, and the analysis shows that Mixup-Inf-Net semi-supervised outperforms most SOTA segmentation models learning framework model in this study, which also enhances segmentation performance. © 2023 SPIE.
RESUMEN
The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the reso-lution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at x3 SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.
RESUMEN
It is important to evaluate medical imaging artificial intelligence (AI) models for possible implicit discrimination (ability to distinguish between subgroups not related to the specific clinical task of the AI model) and disparate impact (difference in outcome rate between subgroups). We studied potential implicit discrimination and disparate impact of a published deep learning/AI model for the prediction of ICU admission for COVID-19 within 24 hours of imaging. The IRB-approved, HIPAA-compliant dataset contained 8,357 chest radiography exams from February 2020-January 2022 (12% ICU admission within 24 hours) and was separated by patient into training, validation, and test sets (64%, 16%, 20% split). The AI output was evaluated in two demographic categories: sex assigned at birth (subgroups male and female) and self-reported race (subgroups Black/African-American and White). We failed to show statistical evidence that the model could implicitly discriminate between members of subgroups categorized by race based on prediction scores (area under the receiver operating characteristic curve, AUC: median [95% confidence interval, CI]: 0.53 [0.48, 0.57]) but there was some marginal evidence of implicit discrimination between members of subgroups categorized by sex (AUC: 0.54 [0.51, 0.57]). No statistical evidence for disparate impact (DI) was observed between the race subgroups (i.e. the 95% CI of the ratio of the favorable outcome rate between two subgroups included one) for the example operating point of the maximized Youden index but some evidence of disparate impact to the male subgroup based on sex was observed. These results help develop evaluation of implicit discrimination and disparate impact of AI models in the context of decision thresholds © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
RESUMEN
This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. The proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. The model is trained with the Signal Processing Grand Challenge (SPGC) 2021 COVID dataset. The proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature. © 2022 ACM.
RESUMEN
Introduction: The need for standardised education on tracheostomy care is well recognised.1 Staff frequently report a lack of confidence in caring for those with tracheostomies, as well as the management of adverse events as they occur.2 Over the past decade, healthcare providers have developed strategies to educate staff, however, the covid-19 pandemic has severely hampered the ability to provide this necessary training due to restrictions on access to training rooms, the need for social distancing and the significant clinical demands placed on both trainers and trainees.3 The potential for immersive technologies to augment healthcare training is gaining interest exponentially.4 However, its effectiveness is yet to be clearly understood and as such it is not yet common within healthcare education.5 Based on the above, we aimed to explore the potential of these immersive technologies to overcome the current challenges of tracheostomy education, and to develop future strategies to use immersive technology in healthcare education. Method(s): We received a 400,000 grant from Cardiff Capital Region (CCR) to undertake a rapid innovation project overseen by the SBRI centre of excellence. The project consisted of 3 main phases: 1) feasibility;2) development;and 3) testing. The project was officially launched in April 2021 and lasted 12 months. Project governance was provided via the SBRI for clinical excellence, a project board with representation from Welsh Government, Cardiff University and Cardiff and Vale UHB, and a project team with clinical expertise in both the delivery of tracheostomy education and the provision of simulation training in healthcare. Result(s): Phase 1: During phase one 4 industries were successful and received up to 30,000 to explore the feasibility of immersive technology to support tracheostomy education. The industries were Rescape, TruCorp, Aspire2Be and Nudge Reality. During the feasibility phase all industries focused on the emergency management process utilising existing NHS Wales tracheostomy education resources and the national tracheostomy safety programme. Phase 2: For phase 2, Rescape and Nudge Reality were chosen to develop the technology. These industries continued to work in conjunction with the project team to capture the core elements of tracheostomy care, including multi-user emergency management scenarios. Additional content was also added for bronchoscopy and insertion of intercostal drains. Phase 3: Testing of both solutions was undertaken over an 8-week period, across 6 Health Boards in NHS Wales. The results of the testing will be analysed and available for presentation in due course. Provision findings demonstrate good face and content validity with high levels of user satisfaction. Discussion / Conclusion(s): The provision of essential tracheostomy education has been severely affected by the covid-19 pandemic. Evolving immersive technologies have the potential to overcome these challenges and improve the effectiveness and efficiency of education packages in tracheostomy care and wider. Through this CCR grant, in conjunction with industry, we have developed two solutions with the potential for widescale procurement and future research on the use of immersive technologies within healthcare.
RESUMEN
The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.
RESUMEN
With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.
RESUMEN
Coronavirus illness, which was initially diagnosed in 2019 but has propagated rapidly across the globe, has led to increased fatalities. According to professional physicians who examined chest CT scans, COVID-19 behaves differently than various viral cases of pneumonia. Even though the illness only recently emerged, a number of research investigations have been performed wherein the progression of the disease impacts mostly on the lungs are identified using thoracic CT scans. In this work, automated identification of COVID-19 is used by using machine learning classifier trained on more than 1000+ lung CT Scan images. As a result, immediate diagnosis of COVID-19, which is very much necessary in the opinion of healthcare specialists, is feasible. To improve detection accuracy, the feature extraction method are applied on regions of interests. Feature extraction approaches, including Discrete Wavelet Transform (DWT), Grey Level Cooccurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), and Grey-Level Size Zone Matrix (GLSZM) algorithms are used. Then the classification by using Support Vector Machines (SVM) is used. The classification accuracy is assessed by using precision, specificity, accuracy, sensitivity and F-score measures. Among all feature extraction methods, the GLCM approach has given the optimum classification accuracy of 95.6%. . © 2023 IEEE.
RESUMEN
The COVID-19 pandemic has made a dramatic impact on human life, medical systems, and financial resources. Due to the disease's pervasive nature, many different and interdisciplinary fields of research pivoted to study the disease. For example, deep learning (DL) techniques were employed early to assess patient diagnosis and prognosis from chest radiographs (CXRs) and computed tomography (CT) scans. While the use of artificial intelligence (AI) in the medical sector has displayed promising results, DL may suffer from lack of reproducibility and generalizability. In this study, the robustness of a pre-trained DL model utilizing the DenseNet-121 architecture was evaluated by using a larger collection of CXRs from the same institution that provided the original model with its test and training datasets. The current test set contained a larger span of dates, incorporated different strains of the virus, and included different immunization statuses. Considering differences in these factors, model performance between the original and current test sets was evaluated using area under the receiver operating characteristic curve (ROC AUC) [95% CI]. Statistical comparisons were performed using the Delong, Kolmogorov-Smirnov, and Wilcoxon rank-sum tests. Uniform manifold approximation and projection (UMAP) was used to help visualize whether underlying causes were responsible for differences in performance between test sets. In the task of classifying between COVID-positive and COVID-negative patients, the DL model achieved an AUC of 0.67 [0.65, 0.70], compared with the original performance of 0.76 [0.73, 0.79]. The results of this study suggest that underlying biases or overfitting may hinder performance when generalizing the model. © 2023 SPIE.
RESUMEN
The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.
RESUMEN
Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The problem of COVID-19 detection has become the world's most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients' Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hybrid model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved by the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerable impact on the model's accuracy. Consequently, it is advised that the model be trained with a sufficient number of epochs to provide reliable classification results. The study's findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images. © 2023 IEEE.
RESUMEN
Introduction: Spontaneous pneumothorax is a rare complication of coronavirus disease 2019 (COVID-19), primarily reported in adults. Pediatric cases with bilateral pneumothorax are much less reported. Case Presentation: We presented the case of a five-year-old previously healthy boy who developed persistent fever, abdominal pain, generalized maculopapular rash, and dyspnea before admission. His chest computed tomography (CT) showed a viral involvement pattern of pneumonia suggestive of COVID-19. Subsequently, he was confirmed with multisystem inflammatory syndrome in children (MIS-C). While he responded well to the therapies, on the fifth day of admission, he developed respiratory distress again. A chest roentgenogram showed bilateral spontaneous pneumothorax. Bilateral chest tubes were inserted, and his condition improved sig-nificantly after five days of admission to the intensive care unit. Two weeks later, he was discharged in good condition. Conclusion(s): Children with MIS-C associated with COVID-19 may develop primary spontaneous pneumothorax. Owing to the clinical picture overlapping with MIS-C associated with COVID-19, the timely diagnosis of pneumothorax may be challenging in such patients.Copyright © 2022, Author(s).
RESUMEN
Background: Sickle cell disease (SCD) is one of the most common single gene disorders worldwide and is characterised by significant morbidity and early mortality.[1] Pregnancy in SCD is associated with an increased risk of maternal and foetal complications.[2,3] The 2011 RCOG and the 2021 BSH guidelines[5,6] on the management of pregnancy in SCD have provided the basis for best practice care in the UK over the past decade and is the guidance which we follow in Ireland. To date, there is no published data on outcomes for pregnant women with SCD in Ireland. The number of Irish patients with SCD has risen over the past 20 years. Without a national database, the exact prevalence is not known but currently there are at least 600 adults and children with SCD in Ireland, whose population is just over 5 million.[4] Aims: Our study assesses outcomes of pregnant patients with SCD from 2015 to 2022. Our aims were to: * Assess adherence to current guidelines * Assess pregnancy outcomes and maternal complications * Assess transfusion rates amongst our patient cohort. Method(s): This is a retrospective cohort study. We do not have a directly matched cohort, but have compared our findings to published data on Irish pregnancy outcomes from the Irish Maternity Indicator System National Report and have correlated our findings with studies of women with SCD who were managed in UK centres.[8,9,10] Results: We reviewed outcomes of 29 pregnancies in 19 women over a 7-year period. The median age was 29 (range 20-41) and the predominant maternal sickle genotype was HbSS (65.5%). Before conception, 55.2% of cases had pre-existing complications of SCD, including acute chest syndrome (ACS), pulmonary hypertension (PHTN) and prior stroke. In accordance with current guidelines, 100% of women (n=29) were prescribed folic acid, penicillin, and aspirin prophylaxis. 51.7% (n=15) of women had documented maternal complications during pregnancy, including ACS (34%), vaso-occlusive crisis (34%), gestational diabetes (10%), VTE (3%) and UTI (3%). Two women (7%) developed Covid-19 pneumonitis despite vaccination. There was one case of maternal bacteraemia (3%). 65.5% of cases (n=19) required blood transfusion during pregnancy. One woman was already on a blood transfusion programme for disease modification prior to pregnancy. In 6 cases (20.6%), a transfusion programme was commenced during pregnancy due to prior pregnancy complications or intrauterine growth restriction. During pregnancy, 27.6% (n=8) of women required emergency red cell exchange for ACS. Prior studies have suggested that between 30% and 70% of pregnant women with SCD require at least one blood transfusion during pregnancy.[8,9,10] By comparison, only 2.6% of the Irish general obstetric population required transfusion during pregnancy.[7] 20.6% (n=6) of births were preterm at <37 weeks' gestation. There was one live preterm birth (3%) at <34 weeks and one intrauterine death (3%) at 23 weeks' gestation. Similar to UK data[9], 31% of women required critical care stay (n=9) during pregnancy, in comparison with 1.44% nationwide in 2020.[7] Conclusion(s): It is well established that pregnancy in SCD is high risk, and despite adherence to current guidelines, we have shown very high rates of critical care admission, significant transfusion requirement and hospital admissions. Our findings are comparable to published UK outcomes and they further support the need for a comprehensive specialist care setting for this patient cohort.
RESUMEN
The arrival of COVID-19 caused devastation to humanity by spreading rapidly around the world and seriously affecting the entire health system. To date, the peculiar symptoms of COVID-19 and the problems it generates in those asthmatic people are already known, which is complicated if they have not had an adequate treatment of their disease, since bronchial asthma is one of the complex bronchopulmonary diseases and for its diagnosis some methods are used that do not provide enough information about the patient's condition, being inefficient methods, therefore, it is necessary to use tools to diagnose pathologies to patients in a comfortable way for an efficient treatment by providing the greatest amount of information about the patient's condition for continuous treatment and in addition to facilitating constant access to several patients with asthma. In view of this problem, in this article a pathology detection system was made in the bronchopulmonary system of asthmatic patients visualized through a radiofrequency of the chest, in such a way that an early diagnosis is made, and some pathological change can be detected in the patient's bronchopulmonary system, with this, an efficient treatment of the patient can be carried out. Through the development of the system, it was possible to observe that the operation was done correctly in the tests conducted, the positioning equipment will move the radiant module on the patient's body for the detection of some pathology with an accuracy of 97.86% efficiency. © 2023 IEEE.
RESUMEN
Today it is observed that few people respect the biosecurity measures announced by the WHO, which aimed to reduce the amount of COVID-19 infection among people, even knowing that this virus has not disappeared from our environment, being an unprecedented infection in the world. It should be noted that before this pandemic, tuberculosis affected millions of people, having a great role because it is highly contagious and directly affects the lungs, although it has a cure, if it is not treated in time it can be fatal for the person, although there are many methods of detection of tuberculosis, one that is most often used is the diagnosis by chest x-ray, although it has low specificity, when the image processing technique is applied, tuberculosis would be accurately detected. In view of this problem, in this article a chest X-ray image processing system was conducted for the early detection of tuberculosis, helping doctors to detect tuberculosis accurately and quickly by having a second opinion by the system in the analysis of the chest x-ray, prevents fatal infections in patients. Through the development of the tuberculosis early detection system, it was possible to observe the correct functioning of the system with an efficiency of 97.84% in the detection of tuberculosis, detailing the characteristics presented by normal or abnormal images so that the doctor detects tuberculosis in the patient early. © 2023 IEEE.
RESUMEN
A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays. © 2023 IEEE.
RESUMEN
Over the past few years, millions of people around the world have developed thoracic ailments. MRI, CT scan, reverse transcription, and other methods are among those used to detect thoracic disorders. These procedures demand medical knowledge and are exceedingly pricy and delicate. An alternate and more widely used method to diagnose diseases of the chest is X-ray imaging. The goal of this study was to increase detection precision in order to develop a computationally assisted diagnostic tool. Different diseases can be identified by combining radiological imaging with various artificial intelligence application approaches. In this study, transfer learning (TL) and capsule neural network techniques are used to propose a method for the automatic detection of various thoracic illnesses utilizing digitized chest X-ray pictures of suspected patients. Four public databases were combined to build a dataset for this purpose. Three pre trained convolutional neural networks (CNNs) were utilized in TL with augmentation as a preprocessing technique to train and evaluate the model. Pneumonia, COVID19, normal, and TB (Tb) were the four class classifiers used to train the network to categorize. © 2023 IEEE.
RESUMEN
This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.