COVID-19 Chest X-ray Research Articles

Advanced Lung Disease Detection: CBAM-Augmented, Lightweight EfficientNetB2 with Visual Insights.

This paper presents a multichannel deep-learning method for detecting lung diseases using chest X-ray images. Using EfficientNetB0 through EfficientNetB7 pretrained models, the methodology offers improved performance in classifying COVID-19, viral pneumonia, and normal chest Xrays. The EfficientNetB2 model was customized by incorporating Squeeze-and-Excitation (SE) blocks and the Convolutional Block Attention Module (CBAM) to improve the model's attention mechanisms. Additional convolutional layers were added for improved feature extraction, and multiscale feature fusion was implemented to capture features at different scales. In this study, 99.3% of the unseen chest X-ray images were identified using the proposed model. It demonstrated superior performance, surpassing existing techniques and highlighting its robustness and generalizability on unseen data samples. Moreover, visualization techniques were used to inspect the intermediate layers of the model, providing deeper insights into its processing and interpretation of medical images. The proposed method offers healthcare radiologists a valuable tool for rapid and accurate point-of-care diagnoses.

BUGTGNN: Bayesian Uncertainty and Game Theory for Enhancing Graph Neural Networks through Mathematical Analysis

The ongoing COVID-19 pandemic has emphasized the critical need for rapid and accurate diagnostic methods. Precise classification of chest X-ray images into COVID-19 and non-COVID-19 cases serves as a pivotal tool in effective disease management and control. Existing methods often suffer from trade-offs between accuracy, precision, and computational efficiency, hindering their practical utility. Current approaches mainly rely on traditional machine learning algorithms or Convolutional Neural Networks (CNNs), which while effective, still present limitations in terms of sensitivity, specificity, and computational speed. These constraints necessitate the exploration of innovative techniques for improving classification metrics across multiple dimensions. In this work, we introduce a novel framework for optimizing Graph Neural Networks (GNNs) through mathematical analysis, specifically incorporating spectral methods, dynamic graph sparsification, game-theoretic attention mechanisms, Bayesian uncertainty models, and advanced graph partitioning techniques. When applied to the classification of COVID-19 chest X-rays, our model demonstrated significant improvements—increasing precision by 8.3%, accuracy by 8.5%, recall by 4.9%, specificity by 4.5%, and the Area Under the Curve (AUC) by 5.9%, while simultaneously reducing computational delay by 10.5% across multiple datasets. The proposed optimization strategies showcase the power of interdisciplinary approaches in advancing machine learning techniques for medical applications. The demonstrated improvements in classification metrics and computational efficiency highlight the model's potential for broader adoption in healthcare settings, providing a robust, fast, and more accurate tool for COVID-19 diagnosis.

Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images

A recent global health crisis, COVID-19 is a significant global health crisis that has profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies using medical images is a challenging task. Thus, the requirement of an end-to-end automated system is vastly necessary in clinical treatments. In this way, the work proposes a Squeeze-and-Excitation Attention-based ResNet50 (SEA-ResNet50) model for detecting COVID-19 utilizing chest X-ray data. Here, the idea lies in improving the residual units of ResNet50 using the squeeze-and-excitation attention mechanism. For further enhancement, the Ranger optimizer and adaptive Mish activation function are employed to improve the feature learning of the SEA-ResNet50 model. For evaluation, two publicly available COVID-19 radiographic datasets are utilized. The chest X-ray input images are augmented during experimentation for robust evaluation against four output classes namely normal, pneumonia, lung opacity, and COVID-19. Then a comparative study is done for the SEA-ResNet50 model against VGG-16, Xception, ResNet18, ResNet50, and DenseNet121 architectures. The proposed framework of SEA-ResNet50 together with the Ranger optimizer and adaptive Mish activation provided maximum classification accuracies of 98.38% (multiclass) and 99.29% (binary classification) as compared with the existing CNN architectures. The proposed method achieved the highest Kappa validation scores of 0.975 (multiclass) and 0.98 (binary classification) over others. Furthermore, the visualization of the saliency maps of the abnormal regions is represented using the explainable artificial intelligence (XAI) model, thereby enhancing interpretability in disease diagnosis.

Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems.

Enhancing Clinical Diagnosis With Convolutional Neural Networks: Developing High-Accuracy Deep Learning Models for Differentiating Thoracic Pathologies.

Background The use of computational technology in medicine has allowed for an increase in the accuracy of clinical diagnosis, reducing errors through additional layers of oversight. Artificial intelligence technologies present the potential to further augment and expedite the accuracy, quality, and efficiency at which diagnosis can be made when used as an adjunctive tool. Such techniques, if found to be accurate and reliable in their diagnostic acuity, can be implemented to foster better clinical decision-making, improving patient quality of care while reducing healthcare costs. Methodology This study implemented convolution neural networks to develop a deep learning model capable of differentiating normal chest X-rays from those indicating pneumonia, tuberculosis, cardiomegaly, and COVID-19. There were 3,063 normal chest X-rays, 3,098 pneumonia chest X-rays, 2,920 COVID-19 chest X-rays, 2,214 chest X-rays, and 554 tuberculosis chest X-rays from Kaggle that were used for training and validation.The model was trained to recognize patterns within the chest X-rays to efficiently recognize these diseases within patients to be treated on time. Results The results indicated a success rate of 98.34% incorrect detections, exemplifying a high degree of accuracy. There are limitations to this study. Training models require hundreds to thousands of samples, and due to potential variability in image scanning equipment and techniques from which the images are sourced, the model could have learned to interpret external noise and unintended details which can adversely impact accuracy. Conclusions Further studies that implement more universal database-sourced images with similar image scanning techniques, assess diverse but related medical conditions, and the utilization of repeat trials can help assess the reliability of the model.These results highlight the potential of machine learning algorithms for disease detection with chest X-rays.

Implementation of CNN of Mobile-based COVID-19 Chest X-Ray Images

The COVID-19 pandemic outbreak is the most significant event from 2019 until 2021. A medical examination of radiological images is carried out to check the condition of the patient's lungs. The limitations of this examination need alternative computer-assisted applications for patient CXR. This research aims to implement a back-end and front-end-based Convolutional Neural Network (CNN) model. Its advantage is that it can detect CXR images in real-time and non-real-time using multi-classification, namely normal, pneumonia, and COVID-19. The CNN model carries out the process of convolutional feature extraction and multi-layer perceptron classification at the back-end stage. In contrast, it uses an Android mobile-based application at the front-end stage. The research results show that the non-real-time condition has an accuracy of 98%, while the real-time is 95% lower. This research produces model and application performance that is flexible for user needs. The results can be recommended for developing applications for more comprehensive users.

A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays

Generalization of deep learning (DL) algorithms is critical for the secure implementation of computer-aided diagnosis systems in clinical practice. However, broad generalization remains to be a challenge in machine learning. This research aims to identify and study potential factors that can affect the internal validation and generalization of DL networks, namely the institution where the images come from, the image processing applied by the X-ray device, and the type of response function of the X-ray device. For these purposes, a pre-trained convolutional neural network (CNN) (VGG16) was trained three times for classifying COVID-19 and control chest radiographs with the same hyperparameters, but using different combinations of data acquired in two institutions by three different X-ray device manufacturers. Regarding internal validation, the addition of images from an external institution to the training set did not modify the algorithm’s internal performance, however, the inclusion of images acquired by a device from a different manufacturer decreased the performance up to 8% (p < 0.05). In contrast, generalization across institutions and X-ray devices with the same type of response function was achieved. Nonetheless, generalization was not observed across devices with different types of response function. This factor was the key impediment to achieving broad generalization in our research, followed by the device’s image-processing and the inter-institutional differences, which both reduced generalization performance to 18.9% (p < 0.05), and 9.8% (p < 0.05), respectively. Finally, clustering analysis with features extracted by the CNN was performed, revealing a substantial dependence of feature values extracted by the pre-trained CNN on the X-ray device which acquired the images.

Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis

Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain model reasoning can potentially increase trust in deep learning among clinical stakeholders. In the literature, much of the research on attribution in medical imaging focuses on visual inspection rather than statistical quantitative analysis.In this paper, we proposed an image-based saliency framework to enhance the explainability of deep learning models in medical image analysis. We use adaptive path-based gradient integration, gradient-free techniques, and class activation mapping along with its derivatives to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.The proposed framework integrates qualitative and statistical quantitative assessments, employing Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) to measure the effectiveness of saliency methods in retaining critical image information and their correlation with model predictions. Visual inspections indicate that methods such as ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produce focused and clinically interpretable attribution maps. These methods highlighted possible biomarkers, exposed model biases, and offered insights into the links between input features and predictions, demonstrating their ability to elucidate model reasoning on these datasets. Empirical evaluations reveal that ScoreCAM and XRAI are particularly effective in retaining relevant image regions, as reflected in their higher AUC values. However, SICs highlight variability, with instances of random saliency masks outperforming established methods, emphasizing the need for combining visual and empirical metrics for a comprehensive evaluation.The results underscore the importance of selecting appropriate saliency methods for specific medical imaging tasks and suggest that combining qualitative and quantitative approaches can enhance the transparency, trustworthiness, and clinical adoption of deep learning models in healthcare. This study advances model explainability to increase trust in deep learning among healthcare stakeholders by revealing the rationale behind predictions. Future research should refine empirical metrics for stability and reliability, include more diverse imaging modalities, and focus on improving model explainability to support clinical decision-making.

Few-shot learning for COVID-19 chest X-ray classification with imbalanced data: an inter vs. intra domain study

Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. This work studies the effect of these challenges at the intra- and inter-domain level in few-shot learning scenarios with severe data imbalance. For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance. Specifically, different initialization and data augmentation methods are analyzed, and four adaptations to Siamese networks of solutions to deal with imbalanced data are introduced, including data balancing and weighted loss, both separately and combined, and with a different balance of pairing ratios. Moreover, we also assess the inference process considering four classifiers, namely Histogram, kNN, SVM, and Random Forest. Evaluation is performed on three chest X-ray datasets with annotated cases of both positive and negative COVID-19 diagnoses. The accuracy of each technique proposed for the Siamese architecture is analyzed separately. The results are compared to those obtained using equivalent methods on a state-of-the-art CNN, achieving an average F1 improvement of up to 3.6%, and up to 5.6% of F1 for intra-domain cases. We conclude that the introduced techniques offer promising improvements over the baseline in almost all cases and that the technique selection may vary depending on the amount of data available and the level of imbalance.

Deep Learning Model for COVID-19 Classification Using Fine Tuned ResNet50 on Chest X-Ray Images

Amid the COVID-19 pandemic, extensive research has focused on deep learning methodologies for accurately diagnosing the virus from chest X-ray images. Various models, including Convolutional Neural Networks (CNNs) and pre-trained models, have achieved accuracies ranging from 85.20% to 99.66%. However, the proposed Fine-Tuned ResNet50 model consistently outperforms others with an impressive accuracy of 98.20%. By leveraging on transfer learning and careful architectural design the proposed model demonstrates superior performance compared to previous studies using DarkNet, ResNet50, and pre-trained models. Graphical comparisons highlight its competitive edge, emphasizing its effectiveness in COVID-19 classification tasks. The ResNet50 architecture, known for its deep residual layers and skip connections, facilitates robust feature extraction and classification, especially in medical imaging. Data pre-processing techniques, like noise reduction and contrast enhancement, ensure input data quality and reliability, enhancing the model&amp;apos;s predictive abilities. Training results reveal the model&amp;apos;s steady accuracy improvement and loss reduction over 20 epochs, aligning closely with validation metrics. Evaluation on a test set of COVID-19 chest X-ray images confirms exceptional accuracy (98.20%), precision (99.00%), recall (98.82%), and F1-score (98.91%), highlighting its proficiency in identifying COVID-19 cases while minimizing false positives and negatives. Comparative analyses against prior studies further validate its superior performance, establishing the Fine-Tuned ResNet50 model as a reliable tool for COVID-19 diagnosis. Future research should focus on exploring ensemble learning techniques, interpretability methods, and stakeholder collaboration to ensure safe AI deployment in clinical settings. Moreover, larger and diverse datasets are crucial for validating model performance and improving generalization, ultimately enhancing patient care and public health outcomes in the mitigating COVID-19 and future pandemics.

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and identifying the one with the highest overall accuracy. First of all, 150 COVID-19 CXR open-source images are acquired from Kaggle and Github. All the images will be unified into a same image size with 1000*1000 pixels and quality during the image pre-processing. Next, the resized images are enhanced by the Modified Global Contrast Stretching (MGCS) enhancement method to increase the quality of images. Then, the traditional k-means, k-medians, k-medoids and fast k-means clustering methods have been implemented in the image segmentation. At the same time, five different numbers &lt;2, 4, 6, 8, 10&gt; of clusters also tested out in this study. Lastly, all the segmented is proceeded to the segmentation performance based on sensitivity, specificity, accuracy, precision, recall and F-score. The result proves that the k-medoids clustering algorithm with 2 clusters archived the best overall segmentation performance as it obtained the highest sensitivity, accuracy, recall and F-score with 66.14%, 87.98%, 0.6614 and 0.7327.

COVID-19 Chest X-Ray Case Detection with Ensemble Deep-Learning

Objectives: The objective of this research is to enhance accuracy on the COVID-19 case identification using X-ray imagery by addressing the drawbacks of utilising a single deep learning model, such as overfitting, high variance, and generalisation errors, by generating predictions with numerous frameworks as opposed to one model. Methods: In this study, secondary data sets from a group of experts from Qatar University in Doha, Qatar, and the University of Dhaka in Bangladesh, together with partners from Pakistan and Malaysia, have produced a dataset of 21,135 CXR pictures for COVID-19 patients, as well as pictures of normal and viral pneumonia. The performance of proposed strategy is, EnDL-COVID-19 is compared with three parameters Accuracy, Sensitivity, PPV Assessment. Findings: ENDL-COVID-19 gives good results for COVID-19, instances identification with a performance of 95%, higher than COVID-Net at 93.3%, are according to research observations ENDL-COVID-19 outperformed by a significant margin in a series of experiments using QU&amp;UD test data consisting of 1592 CXR images. It was able to achieve a sensitivity of 96% and a PPV of 94.1% in determining whether or not COVID-19 occurrences were present. Novelty: The proposed weighted averaging ensemble technique, which is aware of the various sensitivities of deep-learning frameworks on various category types, is used to combine multiple snapshot frameworks of COVIDNet, which made a breakthrough in an open sourced COVID-19 case identification approach using chest X-ray pictures analyzed by deep neural networks. Keywords: COVID-19, Ensembling Learning, Deep-Learning, EnDL-COVID-19, X-Ray Pictures

Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models.

Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.

Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network

COVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA) based Convolutional Neural Network (CNN) method for identifying and classifying COVID-19 chest X-ray images. The DECWOA based CNN model improves the accuracy and convergence speed of the algorithm. This method is evaluated {by} Chest X-Ray (CXR) dataset and attains better results in terms of accuracy, precision, sensitivity, specificity, and F1-score values of about 99.89}%, 99.83%, 99.81%, 98.92%, and 99.26% correspondingly. The result shows that the proposed DECWOA based CNN model provides accurate and quick identification and classification of COVID-19 compared to existing techniques like ResNet50, VGG-19, and Multi-Model Fusion of Deep Transfer Learning (MMF-DTL) models.

Cn2a-capsnet: a capsule network and CNN-attention based method for COVID-19 chest X-ray image diagnosis

Due to its high infectivity, COVID-19 has rapidly spread worldwide, emerging as one of the most severe and urgent diseases faced by the global community in recent years. Currently, deep learning-based diagnostic methods can automatically detect COVID-19 cases from chest X-ray images. However, these methods often rely on large-scale labeled datasets. To address this limitation, we propose a novel neural network model called CN2A-CapsNet, aiming to enhance the automatic diagnosis of COVID-19 in chest X-ray images through efficient feature extraction techniques. Specifically, we combine CNN with an attention mechanism to form the CN2A model, which efficiently mines relevant information from chest X-ray images. Additionally, we incorporate capsule networks to leverage their ability to understand spatial information, ultimately achieving efficient feature extraction. Through validation on a publicly available chest X-ray image dataset, our model achieved a 98.54% accuracy and a 99.01% recall rate in the binary classification task (COVID-19/Normal) on a six-fold cross-validation dataset. In the three-class classification task (COVID-19/Pneumonia/Normal), it attained a 96.71% accuracy and a 98.34% recall rate. Compared to the previous state-of-the-art models, CN2A-CapsNet exhibits notable advantages in diagnosing COVID-19 cases, specifically achieving a high recall rate even with small-scale datasets.

Automated Classification of COVID-19 Chest X-ray Images Using Ensemble Machine Learning Methods

This study delves into the efficacy of ensemble machine learning techniques for classifying chest X-ray images into three distinct categories: Normal, COVID-19, and Lung Opacity. Employing the Random Forest Classifier and a rigorous k-5 cross-validation framework, we aimed to enhance diagnostic accuracy for one of the most urgent medical challenges today—rapid and reliable COVID-19 detection. The analysis revealed an average accuracy of 51%, with varying precision and recall across different folds. The F1-score remained consistently around 35%, indicating a need for improved balance between precision and recall. Visualizations such as performance metric trends and a confusion matrix provided further insight into the classifier's performance, highlighting a notable degree of misclassification. Despite moderate success in the automated classification of the images, our research illustrates the complexity of applying machine learning to medical imaging, especially in differentiating between diseases with overlapping radiographic features. The study’s findings emphasize the potential of machine learning models to support diagnostic processes and suggest the necessity of advanced pre-processing techniques and extended datasets for enhanced model training. The research contributes to the growing body of knowledge in computational diagnostics and underscores the importance of developing robust, accurate machine learning tools to aid in the global healthcare crisis precipitated by the pandemic.

Enhancing medical image segmentation with a multi-transformer U-Net.

Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel approach that combines the Swin Transformer and Deformable Transformer to enhance overall model performance. We leverage the Swin Transformer's window attention mechanism to capture local feature information and employ the Deformable Transformer to adjust sampling positions dynamically, accelerating model convergence and aligning it more closely with object shapes and sizes. By amalgamating both Transformer modules and incorporating additional skip connections to minimize information loss, our proposed model excels at rapidly and accurately segmenting CT or X-ray lung images. Experimental results demonstrate the remarkable, showcasing the significant prowess of our model. It surpasses the performance of the standalone Swin Transformer's Swin Unet and converges more rapidly under identical conditions, yielding accuracy improvements of 0.7% (resulting in 88.18%) and 2.7% (resulting in 98.01%) on the COVID-19 CT scan lesion segmentation dataset and Chest X-ray Masks and Labels dataset, respectively. This advancement has the potential to aid medical practitioners in early diagnosis and treatment decision-making.

A novel compression-based 2D-chaotic sine map for enhancing privacy and security of biometric identification systems

Human biometric images are utilized for cell phone authentication, airport security, and biometric passports. To improve the biometric identification process, this should be protected from cyber attackers because it is sensitive to any minor changes. Thus, security is a major concern in biometric images. Traditional encryption and compression methods are ineffective for encrypting biometric images due to their high execution time and algorithm complexity. In this paper, a novel 2D chaotic sine map is proposed for generating encryption keys and improving security for biometric identification systems. The proposed scheme uses the 2D chaotic map to generate the private key and diffusion process. Here, the Hénon-Sine Map (HSM-512), Secure Hash Algorithm (SHA-256), and DNA computing are also used in the key generation process. The pixel values of the original images in the proposed schemes are shuffled using Mersenne Twister (MT) to improve the security of biometric images. Moreover, the Differential Huffman Compression (DHC) method is used for lossless compression while performing the XOR-based encryption process. The proposed model has been tested on different RGB biometric images, namely the SOCOFing dataset and the COVID-19 chest X-ray dataset. The experiment results have been evaluated using many performance metrics, such as entropy, key space, histogram analysis, key sensitivity, robustness analysis, correlation, and similarity analysis. The outcomes demonstrate that the proposed scheme is more effective than the state-of-the-art schemes.

CXNet - A Novel approach for COVID-19 detection and Classification using Chest X-Ray image

Preliminary screenings are essential to limit the community-propagating nature of COVID-19. COVID-19 patients’ lung health status can be assessed by chest radiographic imaging, such as computed tomography scanning or X-ray images. Therefore, it is preferable to use machine learning approaches to help identify COVID-19 by using chest radiographs. This research presents an image-based diagnosis of COVID-19 disease using deep learning. The presented work uses chest X-ray images because the X-ray imaging facility is widely available in almost all healthcare facilities. It is less costly and has a more negligible radiation effect than computed tomography (CT) scan images. This study employed a custom convolutional neural network (CNN) model and pre- trained deep neural architectures such as resnet50, DenseNet121, VGG16, and VGG19 to classify COVID-19 chest X-ray images. The proposed model has been evaluated on real-life data, and an accuracy of 98% has been achieved. The proposed model can be recommended to health care professionals as a trustworthy diagnostic decision-making system for COVID-19 detection.

Non-iterative fast and efficient HOG-KELM hybrid model for COVID-19 diagnosis using Chest X-ray images

COVID-19 is one of the most widely spread infectious disease, which was declared as pandemic in year 2019 by WHO. The viral infection can be diagnosed using RT-PCR test and through radiological scans, which are time consuming and not 100% accurate. Researchers are trying to develop more reliable, accurate and faster diagnostic system with the help of advance machine learning techniques. Some of the advance models developed using deep learning techniques are efficient but dependent on larger dataset and high-end processing system. In this paper, unique features extracted from COVID-19 chest X-ray images are used for diagnosis with the help of Histogram of Oriented Gradients-Kernel based Extreme Learning Machine (HOG-KELM) model. The proposed model is fast, efficient and economic, which does not rely on heavy machinery or processing unit. The experimental analysis is performed on a dataset of 15,153 CX-ray images, in which 3616 are COVID-19 infected, 10,192 normal lung, and 1345 pneumonia infected images. This model produces 99.78% accuracy for binary classification which evoke the efficacy of the proposed model compared to the existing state-of-art models.