Classification Of Coronavirus Disease 2019 Research Articles

Role of Biomarkers Diagnostic Tools in Patients with COVID-19: Stratification Made Easy.

In coronavirus disease 2019 (COVID-19) patients, several serum biomarkers have been identified. Upon intensive care unit (ICU) admission, these laboratory markers become more crucial to distinguish between patients with severe cases of COVID-19. It might assist doctors in predicting the course of illnesses and treating patients appropriately. This work was to investigate the role of biomarkers in patients with COVID-19 classification admitted to the hospital and identified by reverse transcription polymerase chain reaction (RT-PCR). Peripheral blood sample was taken from COVID-19 cases isolated on admission to determine C-reactive protein (CRP), D-dimer, Fibrinogen, neutrophil-lymphocyte ratio (NLR), leukocytes CRP ratio (LeCR), lymphocyte-CRP ratio (LCR), interleukin-6 (IL6), leukocytes interleukin 6 ratio (LeIL6), systemic inflammatory index (SII), platelet-to-lymphocyte ratio (PLR), and tissue plasminogen activator inhibitor one (tPAI-1). Follow-up for IL6, Ferritin, D-dimer, and tPAI-1 were determined on the 3rd and 7th days. Comparisons of severity revealed that hypertension, chronic obstructive pulmonary disease (COPD), and Ischemia were major risk factors in COVID-19 patients. There was a statistically significant difference between the test groups for fibrinogen (p < 0.000), IL6 (p < 0.009), LeCR (p < 0.006), and LCR (p < 0.011). Based on laboratory test findings at the time of ICU admission, we can distinguish severe cases of COVID-19.

R2C-GAN: Restore-to-Classify Generative Adversarial Networks for blind X-ray restoration and COVID-19 classification

Restoration of poor-quality medical images with a blended set of artifacts plays a vital role in a reliable diagnosis. As a pioneer study in blind X-ray restoration, we propose a joint model for generic image restoration and classification: Restore-to-Classify Generative Adversarial Networks (R2C-GANs). This is the first generic restoration approach forming an Image-to-Image translation task from poor-quality having noisy, blurry, or over/under-exposed images to high-quality image domain where forward and inverse transformations are learned using unpaired training samples. Simultaneously, the joint classification preserves the diagnostic-related label during restoration. Each R2C-GAN is equipped with operational layers/neurons in a compact architecture. The proposed joint model successfully restores images while achieving state-of-the-art Coronavirus Disease 2019 (COVID-19) classification with above 90% in F1-Score. In qualitative analysis, the restoration performance is confirmed by medical doctors where 68% of the restored images are selected against the original images. We share the software implementation at https://github.com/meteahishali/R2C-GAN.

COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.

Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and availability of expert radiologists, as well as limitations of the reverse-transcription polymerase chain reaction (RT-PCR) method. Deep learning, a sophisticated machine learning technique, leverages radiological imaging modalities for disease diagnosis and image classification tasks. Previous research on COVID-19 classification has encountered several limitations, including binary classification methods, single-feature modalities, small public datasets, and reliance on CT diagnostic processes. Additionally, studies have often utilized a flat structure, disregarding the hierarchical structure of pneumonia classification. This study aims to overcome these limitations by identifying pneumonia caused by COVID-19, distinguishing it from other types of pneumonia and healthy lungs using chest X-ray (CXR) images and related tabular medical data, and demonstrate the value of incorporating tabular medical data in achieving more accurate diagnoses. Resnet-based and VGG-based pre-trained convolutional neural network (CNN) models were employed to extract features, which were then combined using early fusion for the classification of eight distinct classes. We leveraged the hierarchal structure of pneumonia classification within our approach to achieve improved classification outcomes. Since an imbalanced dataset is common in this field, a variety of versions of generative adversarial networks (GANs) were used to generate synthetic data. The proposed approach tested in our private datasets of 4523 patients achieved a macro-avg F1-score of 95.9% and an F1-score of 87.5% for COVID-19 identification using a Resnet-based structure. In conclusion, in this study, we were able to create an accurate deep learning multi-modal to diagnose COVID-19 and differentiate it from other kinds of pneumonia and normal lungs, which will enhance the radiological diagnostic process.

A hierarchical fine-grained classification approach for COVID-19 severity assessment based on CT images

The Coronavirus Disease 2019 (COVID-19) is still an ongoing health issue. Appropriate treatment is important for the recovery of COVID-19 patients. At present, numerous deep learning (DL) methods have been applied to the classification of COVID-19 based on computerized tomography (CT) images. However, most of these methods directly extract visual features from the entire CT image, leading to the inclusion of irrelevant background information and making it hard to extract critical features about lesions. Thus, we propose a hierarchical fine-grained classification approach for automatic severity assessment of COVID-19 patients. The CT images are analyzed from coarse to fine in two stages, to focus on the critical features gradually. In the first stage, we detect the lesion regions from the CT image utilizing deep learning based model. In the second stage, we extract critical features from lesion regions and make a severity grading by fine-grained classification model. The experimental results show that the proposed method achieves the classification results of 95.04%, 93.98%, 93.46% and 93.72% in the metrics of accuracy, precision, recall and F1-Score, respectively.

Sex-Specific Total Testosterone and Dehydroepiandrosterone Sulfate Status in Noncritically Ill Hospitalized Patients with Coronavirus Disease 2019: A Cross-Sectional Study.

In individuals with coronavirus disease 2019 (COVID-19), male subjects have consistently been linked to poor severity and prognosis. Data on sex hormones in non-critical COVID-19-infected patients are scarce. The aim of this study was to assess the status of total testosterone (TT) and dehydroepiandrosterone sulfate (DHEAS) among noncritical patients with COVID-19 according to sex and their associations with clinical and biochemical features. This cross-sectional observational study was done in the COVID-19 unit of a University hospital during the period of September 2021 to February 2022 among 91 adults (18-65 years) with reverse transcriptase- polymerase chain reaction confirmed noncritical COVID-19 patients. Blood was drawn by venipuncture before receiving steroids between 07:00 to 09:00 a.m. in a fasting state to measure serum TT and DHEAS by chemiluminescent microparticle immunoassay. Diagnosis and classification of COVID-19 were done according to World Health Organization's interim guidance. Age- and sex-specific laboratory reference values were used to classify the TT and DHEAS status of the patients. Only three males (8.1%) had low TT and the rest had normal TT. On the other hand, 15 (27.8%) of the females had high TT with normal levels in the rest. Similarly, 11 (29.7%) males had low DHEAS. Females had low, normal, and high DHEAS in four (7.4%), 48 (88.9%), and two (3.7%) cases respectively. Males with moderate severity of COVID-19 had significantly lower DHEAS (post hoc P=0.038) than the mild group. Both TT (P=0.008) and DHEAS (P=0.023) significantly correlated with neutrophils/lymphocytes ratio and only DHEAS with platelets/lymphocytes ratio (P=0.044) in males. In females, TT significantly correlated with serum sodium (P=0.034). In noncritical COVID-19 patients, substantial gender variations in TT and DHEAS were detected and correlated with severity markers in males.

Classification of COVID-19 and lung opacity using vision transformer on chest x-ray images

There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT’s is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.

A Hybrid Deep Transfer Learning Model With Kernel Metric for COVID-19 Pneumonia Classification Using Chest CT Images.

Coronavirus disease-2019 (COVID-19) as a new pneumonia which is extremely infectious, the classification of this coronavirus is essential to effectively control the development of the epidemic. Pathological changes in the chest computed tomography (CT) scans are often used as one of the diagnostic criteria of COVID-19. Meanwhile, deep learning-based transfer learning is currently an effective strategy for computer-aided diagnosis (CAD). To further improve the performance of deep transfer learning model used for COVID-19 classification with CT images, in this article, we propose a hybrid model combined with a semi-supervised domain adaption model and extreme learning machine (ELM) classifier, and the application of a novel multikernel correntropy induced loss function in transfer learning is also presented. The proposed model is evaluated on open-source datasets. The experimental results are compared to some baseline models to verify the effectiveness, while adopting accuracy, precision, recall, F1 score and area under curve (AUC) as the evaluation metrics. Experimental results show that the proposed method improves the performance of original model and is more suitable for CT images analysis.

An improved crow search optimization with Bi-LSTM model for identification and classification of COVID-19 infection from chest X-Ray images

Deep learning has become an effective detection method as coronavirus disease 2019 (COVID-19) incidences are increasing quickly. Nevertheless, finding the best accurate models for describing COVID-19 patients is difficult since comparing the outcomes of different data kinds and collecting procedures is difficult. X-ray scans from patients with verified COVID-19 illnessand healthy people were combined to produce a dataset. The dataset's noisy, duplicated, and inappropriate characteristics harm the effectiveness of classification algorithms utilizedin deep learning. As a result, the data should be pre-processed, and essential parts should be chosen to minimize the dimension of datasets by choosing the most crucial qualities while improving classification accuracy. Speed Up-Robust Feature (SURF) method is applied for the feature extraction from the dataset. The required features are selected after feature extraction based on Crow Search Optimization (CSO). The Bi-LSTM mechanism completes the classification procedure at the final stage. Quality metrics like accuracy, recall,precision, and F1-score are contrasted with some other cutting-edge classifiers. The findings demonstrate the effectiveness of the suggested paradigm for COVID-19 identification and classification, demonstrating high specificity, high sensitivity, and low computing complexity.

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%.

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Effect of Different Factors on the Quality of Life and Sequelae of COVID-19 Patients After Discharge

Abstract Background The continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains an international public health emergency, resulting in a significant global disease burden. The long-term effects of SARS-CoV-2 infection in humans and the long-term prognosis of patients with coronavirus disease 2019 (COVID-19) after discharge remain unclear. We aimed to assess the quality of life (QoL) and sequelae in patients with COVID-19 after discharge from the hospital by conducting multiple follow-up visits to understand the long-term effects of SARS-CoV-2 on patients' health and its possible influencing factors. Methods COVID-19 patients discharged from Huoshenshan Hospital (Wuhan, China) between February 15 and April 5, 2020, were followed up at 6, 9, and 12 months after discharge. They completed questionnaires on postdischarge QoL and sequelae under the guidance of medical staff with professional training. The demographic and clinical characteristics of the COVID-19 patients were analyzed using descriptive statistics. A generalized estimating equation model was used to analyze the QoL-related factors. The χ 2 test (or Fisher exact test) and multivariate logistic regression analysis were used to analyze the sequelae and influencing factors. Results A total of 175 patients participated in at least 1 follow-up visit, and 120 completed all 3 follow-up visits. Patients diagnosed with severe and critically ill COVID-19 had worse mental conditions (χ 2 = 7.653, P = 0.022) than those with the nonsevere type (not severe or critical) and were more likely to feel fatigued (χ 2 = 4.836, P = 0.028). Female patients had a higher risk of sleep disturbance (χ 2 = 10.026, P = 0.002) and dyspnea (χ 2 = 5.672, P = 0.017) and had more difficulty returning to their original work and life (χ 2 = 8.922, P = 0.003) than male patients. Patients with diabetes had a worse appetite (χ 2 = 4.669, P = 0.031) and were more prone to sleep disturbance (χ 2 = 4.417, P = 0.036) after discharge. The proportion of patients with at least 1 sequela increased from 29.76% (50/168) at 6 months to 51.11% (69/135) at 9 months (χ 2 = 14.305, P &lt; 0.001). Compared with the nonsevere type, patients diagnosed with severe and critically ill COVID-19 had an odds ratio (OR) of 4.325 (95% confidence interval [CI], 1.215–15.401) for memory decline. Female patients had an OR of 4.632 (95% CI, 1.716–12.501) for joint or muscle pain. Patients with hypertension had an OR of 3.014 (95% CI, 1.193–7.615) for joint or muscle pain. Conclusion One year after discharge, there were still some patients with varying degrees of decline in QoL and sequelae, which occurred in all follow-up visits. Moreover, QoL and sequelae after discharge were related to sex, clinical classification of COVID-19, and underlying diseases.

OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans.

Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Therefore, the classification of computed tomography (CT) scans alleviates the workload of experts, whose workload increased considerably during the pandemic. Convolutional neural network (CNN) architectures are successful for the classification of medical images. In this study, we have developed a new deep CNN architecture called OzNet. Moreover, we have compared it with pretrained architectures namely AlexNet, DenseNet201, GoogleNet, NASNetMobile, ResNet-50, SqueezeNet, and VGG-16. In addition, we have compared the classification success of three preprocessing methods with raw CT scans. We have not only classified the raw CT scans, but also have performed the classification with three different preprocessing methods, which are discrete wavelet transform (DWT), intensity adjustment, and gray to color red, green, blue image conversion on the data sets. Furthermore, it is known that the architecture's performance increases with the use of DWT preprocessing method rather than using the raw data set. The results are extremely promising with the CNN algorithms using the COVID-19 CT scans processed with the DWT. The proposed DWT-OzNet has achieved a high classification performance of more than 98.8% for each calculated metric.

Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography.

To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

A Review of Deep Learning Imaging Diagnostic Methods for COVID-19

COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism; Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19.

Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network

&lt;p&gt;Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convolutional neural network that uses other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU), Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave better results for all performance metrics (accuracy, sensitivity, specificity, precision, and F1-score). The results obtained show that the prediction accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using wavelet filters (rational function with quadratic poles (RASP1), (RASP2), and polynomials windowed (POLYWOG1), superposed logistic function (SLOG1)) as activation function, respectively. Using this algorithm can reduce the time required for the radiologist to detect whether a patient has COVID or not with very high accuracy.&lt;/p&gt;

Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on patients and healthcare systems across the world. Distinguishing non-COVID-19 patients from COVID-19 patients at the lowest possible cost and in the earliest stages of the disease is a major issue. Additionally, the implementation of explainable deep learning decisions is another issue, especially in critical fields such as medicine. The study presents a method to train deep learning models and apply an uncertainty-based ensemble voting policy to achieve 99% accuracy in classifying COVID-19 chest X-rays from normal and pneumonia-related infections. We further present a training scheme that integrates the cyclic cosine annealing approach with cross-validation and uncertainty quantification that is measured using prediction interval coverage probability (PICP) as final ensemble voting weights. We also propose the Uncertain-CAM technique, which improves deep learning explainability and provides a more reliable COVID-19 classification system. We introduce a new image processing technique to measure the explainability based on ground-truth, and we compared it with the widely adopted Grad-CAM method.

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans

BackgroundThe coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial. MethodsA deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP). ResultsLinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods. ConclusionsThe proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.