Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.

A Deep Learning Approach for Detecting Covid-19 Using the Chest X-Ray Images

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now.In the meantime,airspace opacities spreads related to lung have been of the most challenging problems in this area.A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases.Similar to most other classification problems,machine learning-based approaches have been the first/most-used candidates in this application.Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue.In this paper,we develop a novel deep learning architecture to better classify the Covid-19 X-ray images.To do so,we first propose a novel multi-habitat migration artificial bee colony(MHMABC)algorithm to improve the exploitation/exploration of artificial bee colony(ABC)algorithm.After that,we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost.Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters.Furthermore,it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some wellknown benchmark datasets.

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.

Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

单中心新型冠状病毒肺炎(COVID-19)前后期飞行人员肺部CT检查结果分析

目的:目前新型冠状病毒肺炎(COVID-19)大流行已经结束,通过对比2020年1月至12月疫情前期和2023年1月至10月疫情后期飞行人员胸部CT检查结果,观察疫情对飞行人员胸部CT检查结果的影响。方法:回顾性收集了2020年1月至12月和2023年1月至10月在联勤保障部队大连康复疗养中心(以下简称“中心”)共927例男性飞行人员进行的肺部CT检查结果。其中将116例男性飞行人员在2020年1月至12月进行的肺部CT检查结果设为对照组,将810例男性飞行人员在2023年1月至10月进行的肺部CT检查结果设为研究组。根据医学影像学标准,将纳入分析的肺部CT检查结果分为未见明显异常、陈旧性病变、实质结节、玻璃样结节、肺大疱、肺气肿、炎性病变及其他8类,并统计分析了不同时期各种异常情况,并按20~29岁、30~39岁、40~49岁和50~59岁四个年龄组比较了两个时期不同年龄组飞行人员检查结果之间的差异。结论:在两组中,引起异常肺部CT检查结果排名前两位的都是实质结节和陈旧性变化;而在对照组中第三位异常是肺大疱,在研究组中则是肺气肿;与对照组相比,实质结节、陈旧性病变和肺大疱的检出率在研究组中的检出率有所下降;不同年龄组之间异常检查结果的检出率存在统计上的差异(P < 0.001)。

COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its aftermath.Due to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big quantity.Patients who test positive for Covid-19 are diagnosed via a nasal PCR test.In comparison,polymerase chain reaction(PCR)findings take a few hours to a few days.The PCR test is expensive,although the government may bear expenses in certain places.Furthermore,subsets of the population resist invasive testing like swabs.Therefore,chest X-rays or Computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response time.Recent advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest x-rays.This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme.In order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the server.These two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the globalmodel.The proposed model is trained using an image dataset that can be found on Kaggle.There are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are used.Each edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has 5800.There is no association between the datasets of the various nodes that are included in the network.By doing it in this manner,each of the nodes will have access to a separate image collection that has no correlation with each other.The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset,and the findings that we have obtained are quite encouraging.

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020.The consequences of this virus are quite frightening,especially when accompanied by an underlying disease.The novelty of the virus,the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process.Although the new test kits provide almost certain results,chest X-rays are extremely important to detect the progression and degree of the disease.In addition to the Covid-19 virus,pneumonia and harmless opacity of the lungs also complicate the diagnosis.Considering the negative results caused by the virus and the treatment costs,the importance of fast and accurate diagnosis is clearly seen.In this context,deep learning methods appear as an extremely popular approach.In this study,a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease.In addition,in order to contribute to the literature,a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented.With this ensemble model design,quite remarkable results are obtained for the diagnosis of three and four-class Covid-19.The proposed model can classify normal,pneumonia,and Covid-19 with 92.6%accuracy and 82.6%for normal,pneumonia,Covid-19,and lung opacity.

Liver involvement in patients with COVID-19 infection:A comprehensive overview of diagnostic imaging features

During the first wave of the pandemic,coronavirus disease 2019(COVID-19)infection has been considered mainly as a pulmonary infection.However,different clinical and radiological manifestations were observed over time,including involvement of abdominal organs.Nowadays,the liver is considered one of the main affected abdominal organs.Hepatic involvement may be caused by either a direct damage by the virus or an indirect damage related to COVID-19 induced thrombosis or to the use of different drugs.After clinical assessment,radiology plays a key role in the evaluation of liver involvement.Ultrasonography(US),computed tomography(CT)and magnetic resonance imaging(MRI)may be used to evaluate liver involvement.US is widely available and it is considered the first-line technique to assess liver involvement in COVID-19 infection,in particular liver steatosis and portal-vein thrombosis.CT and MRI are used as second-and third-line techniques,respectively,considering their higher sensitivity and specificity compared to US for assessment of both parenchyma and vascularization.This review aims to the spectrum of COVID-19 liver involvement and the most common imaging features of COVID-19 liver damage.

COVID-19 vaccine related hypermetabolic lymph nodes on PET/CT:Implications of inflammatory findings in cancer imaging

We observed several patients presenting 2-[^(18)F]FDG uptake in the reactive axillary lymph node at PET/CT imaging,ipsilateral to the site of the COVID-19 vaccine injection.Analog finding was documented at[^(18)F]Choline PET/CT.The aim of our study was to describe this source of false positive cases.All patients examined by PET/CT were included in the study.Data concerning patient anamnesis,laterality,and time interval from recent COVID-19 vaccination were recorded.SUVmax was measured in all lymph nodes expressing tracer uptake after vaccination.Among 712 PET/CT scans with 2-[^(18)F]FDG,104 were submitted to vaccination;89/104 patients(85%)presented axillary and/or deltoid tracer uptake,related to recent COVID-19 vaccine administration(median from injection:11 days).The mean SUVmax of these findings was 2.1(range 1.6–3.3).Among 89 patients with false positive axillary uptake,36 subjects had received chemotherapy due to lymph node metastases from somatic cancer or lymphomas,prior to the scan:6/36 patients with lymph node metastases showed no response to therapy or progression disease.The mean SUVmax value of lymph nodal localizations of somatic cancers/lymphomas after chemotherapy was 7.8.Only 1/31 prostate cancer patients examined by[^(18)F]Choline PET/CT showed post-vaccine axillary lymph node uptake.These findings were not recorded at PET/CT scans with[^(18)F]-6-FDOPA,[^(68)Ga]Ga-DOTATOC,and[^(18)F]-fluoride.Following COVID-19 mass vaccination,a significant percentage of patients examined by 2-[^(18)F]FDG PET/CT presents axillary,reactive lymph node uptake.Anamnesis,low-dose CT,and ultrasonography facilitated correct diagnosis.Semi-quantitative assessment supported the visual analysis of PET/CT data;SUVmax values of metastatic lymph nodes were considerably higher than post-vaccine lymph nodes.[^(18)F]Choline uptake in reactive lymph node after vaccination was confirmed.After the COVID-19 pandemic,nuclear physicians need to take these potential false positive cases into account in daily clinical practice.

Identifying Severity of COVID-19 Medical Images by Categorizing Using HSDC Model

Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-rays and Computed Tomography(CT)scans.More work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing noise.In addition to the detection of COVID-19,the severity of the infection needs to be estimated.The HSDC model is proposed to solve these problems,which will detect and classify the severity of COVID-19 from X-ray and CT-scan images.For CT-scan images,the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation(ICHSeg)algorithm.Based on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for CT images(HSDCCT)algorithm.When the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep Convolutional Neural Network(DCNN)HSDCX algorithm into COVID-19 or else Pneumonia,or Normal.Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN.This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly,which is the most needed in current years.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

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.

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.

基于CT图像语义的COVID-19实例分割与分类网络

为了辅助临床医生进行COVID-19患者的诊断及治疗,提出了一个从患者肺部CT图像中分类、检测和分割COVID-19病变的辅助诊断网络AIS-Net。首先,该网络将语义分割与实例分割融合,提升了实例分割精度,提出了信息增强注意力模块(IEAM),用于提升输入特征关键信息的权重。为了提高网络对假阴性的关注度,提出了一个实例分割监督方法,用于不同尺度的病变进行监控。其次,设计了一个包含主分类头与辅助分类头的模块,对新冠肺炎、普通肺炎和非肺炎进行分类。在辅助分类中引入了Swin Transformer,提出了区分普通肺炎与新冠肺炎病变的方法。在CC-CCII分割数据集上实例分割的平均精度均值(mAP)为56.53%,比目前最好的方法提升了11.77%;Dice系数、灵敏度、特异度分别为80%,85.1%,99.3%,比目前最好的方法分别提升了4.7%,3.7%,1.2%。在COVIDX-CT分类数据集上实现了99.07%的总体准确度,比目前最好的方法提升了0.92%。AIS-Net可通过CT图像对COVID-19患者进行有效诊断,并对病变部位进行分割及检测。

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 Deep Learning Interpretable Model for Novel Coronavirus Disease (COVID-19) Screening with Chest CT Images

In this article, we propose a convolutional neural network (CNN)-based model, a ResNet-50 based model, for discriminating coronavirus disease 2019 (COVID-19) from Non-COVID-19 using chest CT. We adopted the use of wavelet coefficients of the entire image without cropping any parts of the image as input to the CNN model. One of the main contributions of this study is to implement an algorithm called gradient-weighted class activation mapping to produce a heat map for visually verifying where the CNN model is looking at the image, thereby, ensuring the model is performing correctly. In order to verify the effectiveness and usefulness of the proposed method, we compare the obtained results with that obtained by using pixel values of original images as input to the CNN model. The measures used for performance evaluation include accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and Matthews correlation coefficient (MCC). The overall classification accuracy, F1 score, and MCC for the proposed method (using wavelet coefficients as input) were 92.2%, 0.915%, and 0.839%, and those for the compared method (using pixel values of the original image as input) were 88.3%, 0.876%, and 0.766%, respectively. The experiment results demonstrate the superiority of the proposed method. Moreover, as a comprehensible classification model, the interpretability of classification results was introduced. The region of interest extracted by the proposed model was visualized using heat maps and the probability score was also shown. We believe that our proposed method could provide a promising computerized toolkit to help radiologists and serve as a second eye for them to classify COVID-19 in CT scan screening examination.

An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images

This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge.In this study,CNN architectures such as VGG-16,VGG-19,RestNet50,RestNet18 are compared,and an optimized model for feature extraction in X-ray images from various domains invol-ving several classes is proposed.An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19(Negative or Positive).Then,2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models.Among those,the optimized model architecture classifier technique achieves higher accuracy(0.97)than four other models,specifically VGG-16,VGG-19,RestNet18,and RestNet50(0.96,0.72,0.91,and 0.93,respectively).Therefore,this study will enable radiol-ogists to more efficiently and effectively classify a patient’s coronavirus disease.

不同病程的COVID-19临床与CT影像特征比较分析

目的:比较分析不同病程的新型冠状病毒感染(COVID-19)患者的临床与胸部CT影像特征。方法:回顾性分析2022年12月至2023年1月期间于首都医科大学附属北京世纪坛医院发热门诊收治的161例COVID-19确诊且胸部CT显示肺部感染阳性的病例,按CT检查时发病时间不同分为两组:<10 d及≥10 d,对两组病例的临床表现和胸部CT影像学特征进行统计学分析。结果:<10 d组共92例(57.1%)、≥10 d组共69例(42.9%),两组病例临床症状比较显示,两组间咽痛和肌痛的比例存在统计学差异;实验室指标显示,<10 d组的C反应蛋白更高、淋巴细胞计数更低,其差异存在统计学意义;在CT影像特征方面,<10 d组患者存在血管周、混合分布、大片、空气支气管征的比例更高,≥10 d组患者存在边界不规则、病灶内索条、反晕征、胸膜尾征、胸膜下线、胸膜下栅栏的比例更高,差异有统计学意义。结论:COVID-19的临床症状、实验室指标、CT影像特征随病程不同发生变化,探索其中的规律可以帮助临床医生更好地诊断和治疗COVID-19肺部感染。