Deep learning model for binary classification of COVID-19 based on Chest X-Ray

COVID-19 is a novel virus infecting the upper respiratory tract and lungs. On a scale of the global pandemic, the number of cases and deaths had been increasing each day. Chest X-ray (CXR) images proved effective in monitoring a variety of lung illnesses, including the COVID-19 disease. In recent years, deep learning (DL) has become one of the most significant topics in the computing world and has been extensively applied in several medical applications. In terms of automatic diagnosis of COVID-19, those approaches had proven to be very effective. In this research, a DL technology based on convolution neural networks (CNN) models had been implemented with less number of layers with tuning parameters that will take less time for training for binary classification of COVID-19 based on CXR images. Experimental results had shown that the proposed model for training had achieved an accuracy of 96.68%, Recall of 94.12%, Precision of 93.49%, Specificity of 97.61%, and F1 Score of 93.8%. Those results had shown the high value of utilizing DL for early COVID-19 diagnosis, which can be utilized as a useful tool for COVID-19 screening. © 2023 IEEE.

Automatic Diagnosis of COVID-19 Based on Machine Learning

The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.

Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset.

One of the main challenges in the current pandemic is the detection of coronavirus. Conventional techniques (PT-PCR) have their limitations such as long response time and limited accessibility. On the other hand, X-ray machines are widely available and they are already digitized in the health systems. Thus, their usage is faster and more available. Therefore, in this research, we evaluate how well deep CNNs do when it comes to classifying normal versus pathological chest X-rays. Compared to the previous research, we trained our network on the largest number of images, 103,468 in total, including 5 classes such as COPD signs, COVID, normal, others and Pneumonia. We achieved COVID accuracy of 97% and overall accuracy of 81%. Additionally, we achieved classification accuracy of 84% for categorization into normal (78%) and abnormal (88%).

An Optimized Deep Learning Model for Automatic Diagnosis of COVID-19 Using Chest X-Ray Images

COVID-19 has caused havoc throughout the world in the last two years by infecting over 455 million people. Development of automatic diagnosis software tools for rapid screening of COVID-19 via clinical imaging such as X-ray is vital to combat this pandemic. An optimized deep learning model is designed in this paper to perform automatic diagnosis on the chest X-ray (CXR) images of patients and classify them into normal, pneumonia and COVID-19 cases. A convolutional neural network (CNN) is employed in optimized deep learning model given its excellent performances in feature extraction and classification. A particle swarm optimization with multiple chaotic initialization scheme (PSOMCIS) is also designed to fine tune the hyperparameters of CNN, ensuring the proper training of network. The proposed deep learning model, namely PSOMCIS-CNN, is evaluated using a public database consists of the CXR images with normal, pneumonia and COVID-19 cases. The proposed PSOMCIS-CNN is revealed to have promising performances for automatic diagnosis of COVID-19 cases by producing the accuracy, sensitivity, specificity, precision and F1 score values of 97.78%, 97.77%, 98.8%, 97.77% and 97.77%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A MHA-based integrated diagnosis and segmentation method for COVID-19 pandemic

Objective In order to alleviate the COVID-19 (corona virus disease 2019) pandemic, the initial implementation is focused on targeting and isolating the infectious patients in time. Traditional PCR (polymerase chain reaction) screening method is challenged for the costly and time-consuming problem. Emerging AI (artificial intelligence) -based deep learning networks have been applied in medical imaging for the COVID-19 diagnosis and pathological lung segmentation nowadays. However, current networks are mostly restricted by the experimental datasets with limited number of chest X-ray (CXR) images, and it merely focuses on a single task of diagnosis or segmentation. Most networks are based on the convolution neural network (CNN). However, the convolution operation of CNN is capable to extract local features derived from intrinsic pixels, and has the long-range dependency constraints for explicitly modeling. We develop a vision transformer network (ViTNet). The multi-head attention (MHA) mechanism is guided for long-range dependency model between pixels. Method We built a novel transformer network called ViTNet for diagnosis and segmentation both. The ViTNet is composed of three parts, including dual-path feature embedding, transformer module and segmentation-oriented feature decoder. 1) The embedded dual-path feature is based on two manners for the embedded CXR inputs. One manner is on the basis of 2D convolution with the sliding step equal to convolution kernel size, which divides a CXR to multiple patches and builds an input vector for each patch. The other manner is concerned of a pre-trained feature map (ResNet34-derived) as backbone in terms of deep CXR-based feature extraction. 2) The transformer module is composed of six encoders and one cross-attention module. The 2D-convolution-generated vector sequence is as inputs for transformer encoder. Owing that the encoder inputs are directly extracted from image pixels, they can be considered as the shallow and intuitive feature of CXR. The six encodes are in sequential, transforming the shallow feature to advanced global feature. The cross-attention module is focused on the results obtained by backbone and transformer encoders as inputs, the network can combine the deep feature and encoded shallow feature, and absorb both the global information and the local information in terms of the encoded shallow feature and deep feature, respectively. 3) The feature decoder for segmentation can double the size of feature map and provide the segmentation results. Our network is required to deal with two tasks simultaneously for both of classification and segmentation. A hybrid loss function is employed for their training, which can balance the training efforts between classification and segmentation. The classification loss is the sum of a contrastive loss and a multi-classes cross-entropy loss. The segmentation loss is a binary cross-entropy loss. What is more, a new five-levels CXR dataset is compiled. The dataset samples are based on 2 951 CXRs of COVID-19, 16 964 CXRs of healthy, 6 103 CXRs of bacterial pneumonia, 5 725 CXRs of viral pneumonia, and 6 723 CXRs of opaque lung. In this dataset, COVID-19 CXRs are all labeled with COVID-19 infected lung masks. In our training process, the input images were resized as 448 × 448 pixels, the learning rate is initially set as 2 × 10 - 4 and decreased gradually in a self-adaptive manner, and the total number of iterations is 200, the Adam learning procedure is conducted on four Tesla K80 GPU devices. Result In the classification experiments, we compared ViTNet to a general transformer network and five popular CNN deep-learning models (i. e., Res-Net18, ResNet50, VGG16 (Visual Geometry Group), Inception _ v3, and deep layer aggregation network (DLAN) in terms of overall prediction accuracy, recall rate, F1 and kappa evaluator. It can be demonstrated that our model has the best with 95. 37% accuracy, followed by Inception_ v3 and DLAN with 95. 17% and 94. 40% accuracy, respectively, and the VGG16 is reached 94. 19% ac uracy. For the recall rate, F1 and kappa value, our model has better performance than the rest of networks as well. For the segmentation experiments, ViTNet is in comparison with four commonly-used segmentation networks like pyramid scene parsing network (PSPNet), U-Net, U-Net + and context encoder network (CE-Net) . The evaluation indicators used are derived of the accuracy, sensitivity, specificity, Dice coefficient and area under ROC (region of interest) curve (AUC) . The experimental results show that our model has its potentials in terms of the accuracy and AUC. The second best sensitivity is performed inferior to U-Net + only. More specifically, our model achieved the 95. 96% accuracy, 78. 89% sensitivity, 97. 97% specificity, 98. 55% AUC and a Dice coefficient of 76. 68% . When it comes to the network efficiency, our model speed is 0. 56 s per CXR. In addition, we demonstrate the segmentation results of six COVID-19 CXR images obtained by all the segmentation networks. It is reflected that our model has the best segmentation performance in terms of the illustration of Fig. 5. Our model limitation is to classify a COVID-19 group as healthy group incorrectly, which is not feasible. The PCR method for COVID-19 is probably more trustable than the deep-leaning method, but the feedback duration of tested result typically needs for 1 or 2 days. Conclusion A novel ViTNet method is developed, which achieves the auto-diagnosis on CXR and lung region segmentation for COVID-19 infection simultaneously. The ViTNet has its priority in diagnosis performance and demonstrate its potential segmentation ability. © 2022 Editorial and Publishing Board of JIG. All rights reserved.

COVID-19 Infection Forecasting using LSTM-based Model

COVID-19 has now become one of the most severe and acute diseases worldwide. Novel Coronavirus transmission is characterized by its high speed and large social population base, making novel Coronavirus detection very difficult. Therefore, automatic detection systems should be implemented as an option for rapid diagnosis. Automated disease detection frameworks help physicians diagnose diseases with accurate, consistent, and rapid results, and reduce ethics. In this paper, we propose a deep learning method based on long-term Memory (LSTM) for automatic diagnosis of COVID-19 in combination with the existing prediction model SEIR. © 2022 SPIE.

Deep Dual Attention Network for Precise Diagnosis of COVID-19 From Chest CT Images

Automatic diagnosis of COVID-19 using chest CT images is of great significance for preventing its spread. However, it is difficult to precisely identify COVID-19 due to the following problems: 1) the location and size of lesions can vary greatly in CT images;2) its unique characteristics are often imperceptible in imaging findings. To solve these problems, a Deep Dual Attention Network (<inline-formula><tex-math notation="LaTeX">$\textrm {D}

Automatic diagnosis of COVID-19 using deep learning

The COVID-19 pandemic is an unprecedented global health crisis. Given the delays in obtaining RT-PCR results, the reference diagnostic technique, CT (computed tomography) plays a central role in triaging patients arriving in the emergency department, allowing them to be admitted to "COVID"or "non-COVID"services. To make reliable automatic diagnoses and to quantify the lesion extent, we used deep learning methods for the diagnosis of COVID-19. In this work, we implemented six CNN architectures, namely VGG16, ResNet50, MobileNet, GoogLeNet, Xception, and DenseNet121, and evaluated their performance using appropriate metrics on CT images from freely available public databases. The ResNet50 model was found to give the best results for acceptable computational complexity. © 2022 IEEE.

Covid-19 detection from X-ray scans using Alexa

COVID 19 is a corona virus-related ailment. A global pandemic has commenced over the fatality of the virus. The gravity of the situation is taken into account by medical professionals across the globe. Along with the Covid protocols being implemented, detection at the onset of the illness allows patients to isolate themselves, reducing the risk of infection. Lately, Chest X ray scans are invaluable when it comes to COVID 19 detection. On account of their quicker imaging time, extensive availability, budget-friendly, and portability have reaped a great deal of attention and become very promising. COVID 19, owing to its tortuous mutation, is an enigma and, hence a timely and automatic diagnosis would be instrumental in helping professionals. Virtual assistants allow users to communicate in natural language, they are used to supplement health service capacity and lower exposure. Therefore, to predict Covid-19 disease we employed the chest X-ray image data-set and implemented the CNN model of Deep Learning along with the Alexa Speech recognition skill set. © 2022 IEEE.

Transfer Learning for Automatic Detection of COVID-19 Disease in Medical Chest X-ray Images

The world has experienced the spread of a dangerous virus, Coronavirus (COVID-19), that has caused the death of millions of people worldwide at an extremely rapid rate, many studies have confirmed that the virus can be detected effectively using medical images. However, it takes a long time to analyze each image by radiologists who suffer from high pressures, especially due to the high similarity of symptoms between this virus and other respiratory diseases, which can lead to the confusion of cases and, consequently, the inability to identify them quickly, which could be a problem in a pandemic situation. In this paper, a methodology is proposed for the rapid and automatic diagnosis of this virus from chest radiographic images through the use of Artificial Intelligence (AI) techniques. There are two stages of the proposed model. The first step is data augmentation and preprocessing;the second step is the detection of COVID-19 with a transfer learning technique using a pre-trained deep convolutional network (CNN) architecture to extract features, Then, the obtained feature vectors are classified into three classes: COVID-19, Normal, and pneumonia, from two open medical repositories. In the experimentation phase of our model, we evaluate a set of common metrics to measure the performance of the architecture. Experimental conclusions show an accuracy of 96.52% for all classes, then a comparison with existing models in literature demonstrates that our proposed model achieves better classification accuracy © 2022. IAENG International Journal of Computer Science.All Rights Reserved.

Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM

The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively. © 2022 IEEE.

Automatic Diagnosis of COVID-19 Medical Images based on Graph Attention Network

In view of the COVID-19 pandemic and its highly infectious characteristic, traditional artificial diagnosis based on medical imaging, though capable of detecting pulmonary lesion in human body, is found of lower efficiency. Therefore, it is particularly urgent that we design a set of accurate and automatic pneumonia diagnosis methods with aid of artificial intelligence technology, so that pneumonia in patients can be diagnosed and treated early. This study first introduces DenseNet to the Convolutional Neural Network (CNN) structure to improve sharing of characteristic information of lung image in convolutional layers and thus obtain more accurate image features. Secondly, characteristics of pneumonia disease are discriminated rapidly using the Graphic Attention Network (GAT). The authors adopt the X-ray dataset in Radiological Society of North America (RSNA) Pneumonia Detection Challenge released by Kaggle to train and verify the network. According to experimental results, the accuracy of COVID-19 diagnosis and F-Score both reach 98%. The method provides CT doctors with an end-to-end deep learning technology for pneumonia diagnosis. © 2021 IEEE.

Artificial Intelligence-Based Phonocardiogram: Classification Using Cepstral Features

When cardiovascular issues arise in a cardiac patient, it is essential to diagnose them as soon as possible for monitoring and treatment would be less difficult than in the old. Paediatric cardiologists have a difficult time keeping track of their patients’ cardiovascular condition. To accomplish this, a phonocardiogram (PCG) device was created in combination with a MATLAB software based on artificial intelligence (AI) for automatic diagnosis of heart state classification as normal or pathological. Due to the safety concerns associated with COVID-19, testing on school-aged children is currently being explored. Using PCG analyses and machine learning methods, the goal of this work is to detect a cardiac condition, whilst operating on a limited amount of computing resources. This makes it possible for anybody, including non-medical professionals, to diagnose cardiac issues. To put it simply, the current system consists of a distinct portable electronic stethoscope, headphones linked to the stethoscope, a sound-processing computer, and specifically developed software for capturing and analysing heart sounds. However, this is more difficult and time-consuming, and the accuracy is lowered as a result. According to statistical studies, even expert cardiologists only achieve an accuracy of approximately 80%. Nevertheless, primary care doctors and medical students usually attain a level of accuracy of between 20 and 40%. Due to the nonstationary nature of heart sounds and PCG's superior ability to model and analyse even in the face of noise, PCG sounds provide valuable information regarding heart diseases. Spectral characteristics PCG is used to characterise heart sounds in order to diagnose cardiac conditions. We categorise normal and abnormal sounds using cepstral coefficients, or PCG waves, for fast and effective identification, prompted by cepstral features’ effectiveness in speech signal classification. On the basis of their statistical properties, we suggest a new feature set for cepstral coefficients. The PhysioNet PCG training dataset is used in the experiments. This section compares KNN with SVM classifiers, indicating that KNN is more accurate. Furthermore, the results indicate that statistical features derived from PCG Mel-frequency cepstral coefficients outperform both frequently used wavelet-based features and conventional cepstral coefficients, including MFCCs. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images

The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy. Rapid and accurate diagnosis of COVID-19 is essential to prevent the further spread of the disease and reduce its mortality. Chest Computed tomography (CT) is an effective tool for the early diagnosis of lung diseases including pneumonia. However, detecting COVID-19 from CT is demanding and prone to human errors as some early-stage patients may have negative findings on images. Recently, many deep learning methods have achieved impressive performance in this regard. Despite their effectiveness, most of these methods underestimate the rich spatial information preserved in the 3D structure or suffer from the propagation of errors. To address this problem, we propose a Dual-Attention Residual Network (DARNet) to automatically identify COVID-19 from other common pneumonia (CP) and healthy people using 3D chest CT images. Specifically, we design a dual-attention module consisting of channel-wise attention and depth-wise attention mechanisms. The former is utilized to enhance channel independence, while the latter is developed to recalibrate the depth-level features. Then, we integrate them in a unified manner to extract and refine the features at different levels to further improve the diagnostic performance. We evaluate DARNet on a large public CT dataset and obtain superior performance. Besides, the ablation study and visualization analysis prove the effectiveness and interpretability of the proposed method. © 2021 IEEE.

Survey on using deep learning to detection COVID-19 disease

The spread of COVID-19 disease rapidly worldwide and the increase in deaths are a threat to humanity. This threat prompted researchers in deep learning (DL) to find ways to diagnose COVID-19 from computed tomography (CT) or x-rays. Working deep learning identifies the infection accurately through medical imaging, and the practising radiologist can diagnose the illness. This survey will discuss the reason behind deep learning and the technology used in medical image processing. Exposure to the most common research in the recent period uses deep learning techniques in the medical field. We will then collect research related to diagnosing COVID-19 by using medical images, studying them, discussing the better future suggestion and methods proposed by other researchers. We focus on initial available research that detects COVID-19 by deep learning and sees how they can save time and effort in this field. © 2021 IEEE

SVM and ANN classification using GLCM and HOG features for COVID-19 and Pneumonia detection from Chest X-rays

Due to the coronavirus pandemic and the lack of an automatic COVID-19 diagnostic system to relieve congestion in health centers and to support the traceability of this disease, this article exposes the implementation of algorithms for automatic diagnosis of lung diseases such as COVID-19 and Pneumonia from chest X-rays (CXR) through GLCM and HOG features extraction using 6300 patches. Then, selecting the best features and different classifiers such as an Support Vector Machine (SVM) and Artificial Neural Network (ANN) to obtain a system maximum accuracy of 93,73% for SVM. © 2021 IEEE.

Ai covid: Automatic diagnosis of covid-19 using frontal chest x-ray image

With the continued growth of confirmed cases of COVID-19, a highly infectious disease caused by a newly discovered coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2, or SARS-CoV-2, there is an urgent need to find ways to help clinicians fight the virus by reducing the workload and speeding up the diagnosis of COVID-19. In this work, we propose an artificial intelligence solution "AI COVID"which can help radiologists to know if the lungs are infected with the virus in just a few seconds.AI COVID is based on a pre-Trained DenseNet-121 model that detects subtle changes in the lungs and an SVM classifier that decides whether these changes are caused by COVID-19 or other diseases. AI COVID is trained on thousands of frontal chest x-rays of people who have contracted COVID-19, healthy people, and people with viral or bacterial pneumonia. The experimental study is tested on 781 chest x-rays from two publicly available chest x-ray datasets COVID-19 radiography database and COVIDx Dataset. The performance results showed that our proposed model (DenseNet-121 + SVM) demonstrated high performance and yielded excellent results compared to the current methods in the literature, with a total accuracy of 99.74% and 98.85% for binary classification (COVID-19 vs. No COVID-19) and multi-class classification (COVID-19 vs. Normal vs. Pneumonia), respectively. © Author(s) 2021. CC BY 4.0 License.

Automatic diagnosis of coronavirus (COVID-19) using shape and texture characteristics extracted from X-Ray and CT-Scan images.

Automatic diagnosis of coronavirus (COVID-19) is studied in this research. Deep learning methods especially convolutional neural networks (CNNs) have shown great success in COVID-19 diagnosis in recent works. But they are efficient when the depth of network is high enough. However, the use of a deep network requires a sufficiently large training set, which is not available in practice. From the other hand, the use of a shallow CNN may not provide superior results because it is not able to rich feature extraction due to lacking enough convolutional layers. To deal with this difficulty, the contextual features reduced by convolutional filters (CFRCF) is proposed in this work. CFRCF extracts shape and textural features as contextual feature maps from the chest X-ray radiographs and abdominal computed tomography (CT) images. Morphological operators, Gabor filter banks and attribute filters are used for contextual feature extraction. Then, two convolutional filters are applied to the contextual feature cube to extract the nonlinear sub-features and hidden relationships among the contextual features. Finally, a fully connected layer is used to produce a reduced feature vector which is fed to a classifier. Support vector machine and random forest are used as classifier. The experimental results show the superior performance of the proposed method from the recognition accuracy and running time point of view using limited training samples. More than 76% and 94% overall classification accuracy is obtained by the proposed method in CT scan and X-ray images datasets, respectively.

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network.

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network.

The novel coronavirus disease (COVID-19) spread quickly worldwide, changing the everyday lives of billions of individuals. The preliminary diagnosis of COVID-19 empowers health experts and government professionals to break the chain of change and level the epidemic curve. The regular sort of COVID-19 detection test, be that as it may, requires specific hardware and generally has low sensitivity. Chest X-ray images to be used to diagnosis the COVID-19. In this work, a dataset of X-ray images with COVID-19, bacterial pneumonia, and normal was used to diagnose the COVID-19 automatically. This work to assess the execution of best in class Convolutional Neural Network (CNN) models proposed over ongoing years for clinical image classification. In particular, the modified pre-trained CNN-ResNet50 based Extreme Learning Machine classifier (ELM) has proposed for different diagnosis abnormalities such as COVID-19, Pneumonia, and normal. The proposed CNN method has trained and tested with the publicly available COVID-19, pneumonia, and normal datasets. The presented pre-trained ResNet CNN model provides accuracy, sensitivity, specificity, recall, precision, and F1 score values of 94.07, 98.15, 91.48, 85.21, 98.15, and 91.22, respectively, which is the best classification performance than other states of the art methods. This study introduced a computationally productive and exceptionally exact model for multi-class grouping of three diverse contamination types from alongside Normal people. This CNN model can help in the automatic diagnosis of COVID-19 cases and help decrease the burden on medicinal services frameworks.