Chest X-ray Image Classification Research Articles

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.

Multi-disease X-ray Image Classification of the Chest Based on Global and Local Fusion Adaptive Networks

Chest X-ray image classification for multiple diseases is an important research direction in the field of computer vision and medical image processing. It aims to utilize advanced image processing techniques and deep learning algorithms to automatically analyze and identify X-ray images, determining whether specific pathologies or structural abnormalities exist in the images. We present the MMPDenseNet network designed specifically for chest multi-label disease classification. Initially, the network employs the adaptive activation function Meta-ACON to enhance feature representation. Subsequently, the network incorporates a multi-head self-attention mechanism, merging the conventional convolutional neural network with the Transformer, thereby bolstering the ability to extract both local and global features. Ultimately, the network integrates a pyramid squeeze attention module to capture spatial information and enrich the feature space. The concluding experiment yielded an average AUC of 0.898, marking an average accuracy improvement of 0.6% over the baseline model. When compared with the original network, the experimental results highlight that MMPDenseNet considerably elevates the classification accuracy of various chest diseases. It can be concluded that the network, thus, holds substantial value for clinical applications.

Uncertainty quantification in multi-class image classification using chest X-ray images of COVID-19 and pneumonia.

This paper investigates uncertainty quantification (UQ) techniques in multi-class classification of chest X-ray images (COVID-19, Pneumonia, and Normal). We evaluate Bayesian Neural Networks (BNN) and the Deep Neural Network with UQ (DNN with UQ) techniques, including Monte Carlo dropout, Ensemble Bayesian Neural Network (EBNN), Ensemble Monte Carlo (EMC) dropout, across different evaluation metrics. Our analysis reveals that DNN with UQ, especially EBNN and EMC dropout, consistently outperform BNNs. For example, in Class 0 vs. All, EBNN achieved a UAcc of 92.6%, UAUC-ROC of 95.0%, and a Brier Score of 0.157, significantly surpassing BNN's performance. Similarly, EMC Dropout excelled in Class 1 vs. All with a UAcc of 83.5%, UAUC-ROC of 95.8%, and a Brier Score of 0.165. These advanced models demonstrated higher accuracy, better discriaminative capability, and more accurate probabilistic predictions. Our findings highlight the efficacy of DNN with UQ in enhancing model reliability and interpretability, making them highly suitable for critical healthcare applications like chest X-ray imageQ6 classification.

MultiFusionNet: multilayer multimodal fusion of deep neural networks for chest X-ray image classification

MultiFusionNet: multilayer multimodal fusion of deep neural networks for chest X-ray image classification

Hybrid deep features computed from spatial images and bit plane-based pattern maps for the classification of chest X-ray images

Chest X-ray images are known to be extremely helpful in the investigation of a numerous pulmonary conditions such as COVID-19 and pneumonia. Many affected individuals may be protected from such pulmonary conditions through early identification. Unfortunately, COVID-19 can be misdiagnosed as pneumonia, which can rapidly worsen and lead to death. The sensitivity of RT-PCR-based COVID-19 detection is also not satisfactory. Herein, a deep-learning (DL) model for predicting three distinct classes, i.e., COVID-19, pneumonia, and normal, is presented, which achieves good classification performance. Three separate publicly available datasets and an additional dataset with their merged form were used to confirm the efficacy of the proposed model. The DL-based techniques compute features from original raw input spatial images and do not directly provide much information on extremely fine image details, which is quite important for biomedical image analysis. As the individual image bit planes (BPs) carry extremely-fine-to-coarse image information and the effective handcrafted pattern maps created from these BPs may incorporate important discriminating information, the deep features computed from such handcrafted pattern maps may provide complementary information regarding the deep features computed using raw spatial input images. Therefore, we propose a blend of deep features computed with raw spatial images and deep features computed using the proposed local bit plane-based pattern maps to predict the three classes. It is demonstrated that the blend of such features supplies improved discrimination potential and is complementary to the sole features. We incorporated multiscale information computed in each BPs along with interscale details to generate the final bit plane-based pattern maps. The proposed model achieved an average accuracy of 100%, 99.9%, 98.8%, and 98.8% for datasets 1,2, and 3 and their combined form, respectively, outperforming the existing methods.

Improving Chest X-ray Image Classification via Integration of Self-Supervised Learning and Machine Learning Algorithms

Improving Chest X-ray Image Classification via Integration of Self-Supervised Learning and Machine Learning Algorithms

RESNET101 AND GOOGLENET DEEP LEARNING MODELS: COMPARING SUCCESS LEVELS IN THE HEALTH SECTOR

Artificial intelligence (AI) applications in the healthcare sector have revolutionized medical diagnosis and treatment. Advances in this field provide many advantages such as early detection of diseases and increasing the efficiency of healthcare services. In this study, in order to investigate the usability of deep learning models for tuberculosis (TB) detection, the accuracy rates of deep learning models such as ResNet101 and GoogLeNet are compared in terms of TB detection potential in the healthcare sector. The results of the analyses revealed that deep learning networks are successful in classifying chest X-ray images with and without TB. In addition, when the success levels were analyzed, it was determined that the ResNet101 deep learning network, with a success rate of 99.3%, showed a higher score than the other deep learning model considered in the study, GoogLeNet (98.2%). These findings obtained within the scope of the research reveal the importance and functionality of AI applications in order to increase diagnostic accuracy rates.

Tuning VGG19 hyperparameters for improved pneumonia classification

This research focuses on the classification of chest X-ray (CXR) images using powerful VGG19 convolutional neural network (CNN)architecture. The classification task involves distinguishing between various chest conditions present in X-ray images, with the aim of assisting medical professionals in achieving accurate and efficient diagnoses. This research work explores the use of the VGG19 model for classifying CXR images using three optimization algorithms: Stochastic gradient descent with momentum (SGDM), root mean square propagation (RMSprop), and adaptive moment estimation (Adam). This study investigates the impact of various factors on hyperparameter adjustments, including a learning rate (LR), mini-batch size (MBS) and training epochs. Additionally, two dropout layers are introduced for weight decay with an L2 factor, and data augmentation techniques are applied with various activation functions. This study not only helps optimize for medical image analysis but also offers valuable insights into the comparative efficacy of popular optimization algorithms in deep learning (DL) applications

COVID‑19 detection from chest X-ray images using transfer learning

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.

Texture-Based Classification to Overcome Uncertainty between COVID-19 and Viral Pneumonia Using Machine Learning and Deep Learning Techniques

The SARS-CoV-2 virus, responsible for COVID-19, often manifests symptoms akin to viral pneumonia, complicating early detection and potentially leading to severe COVID pneumonia and long-term effects. Particularly affecting young individuals, the elderly, and those with weakened immune systems, the accurate classification of COVID-19 poses challenges, especially with highly dimensional image data. Past studies have faced limitations due to simplistic algorithms and small, biased datasets, yielding inaccurate results. In response, our study introduces a novel classification model that integrates advanced texture feature extraction methods, including GLCM, GLDM, and wavelet transform, within a deep learning framework. This innovative approach enables the effective classification of chest X-ray images into normal, COVID-19, and viral pneumonia categories, overcoming the limitations encountered in previous studies. Leveraging the unique textures inherent to each dataset class, our model achieves superior classification performance, even amidst the complexity and diversity of the data. Moreover, we present comprehensive numerical findings demonstrating the superiority of our approach over traditional methods. The numerical results highlight the accuracy (random forest (RF): 0.85; SVM (support vector machine): 0.70; deep learning neural network (DLNN): 0.92), recall (RF: 0.85, SVM: 0.74, DLNN: 0.93), precision (RF: 0.86, SVM: 0.71, DLNN: 0.87), and F1-Score (RF: 0.86, SVM: 0.72, DLNN: 0.89) of our proposed model. Our study represents a significant advancement in AI-based diagnostic systems for COVID-19 and pneumonia, promising improved patient outcomes and healthcare management strategies.

Comparison of deep learning models based on Chest X-ray image classification

Comparison of deep learning models based on Chest X-ray image classification

Application of Ensemble Learning in CXR Classification for Improving COVID-19 Diagnosis

This study delves into the vital task of classifying chest X-ray (CXR) samples, particularly those related to respiratory ailments, using advanced clinical image analysis and computer-aided radiology techniques. Its primary focus is on developing a classifier to accurately identify COVID-19 cases. Through the application of machine learning and computer vision methodologies, the research aims to enhance the precision of COVID-19 detection. It investigates the effectiveness of Histogram of Oriented Gradients (HOG) feature extraction techniques in conjunction with various classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), K-nearest neighbor (KNN), and Tree Bagger (TB), alongside an innovative ensemble learning approach. Results indicate impressive accuracy rates, with KNN, SVM, DT, NB, and TB all surpassing the 90% mark. However, the ensemble learning method emerges as the standout performer. By leveraging HOG features extracted from CXR images, this approach presents a robust solution for COVID-19 diagnosis, offering a powerful tool to address the diagnostic challenges posed by the pandemic.

An effective ensemble approach for classification of chest X-ray images having symptoms of COVID: A precautionary measure for the COVID-19 subvariants

In recent years many people have died due to COVID-19 infection. The death rate increases due to the lack of proper diagnosis and treatment. It can be detected by RT-PCR test and from chest X-rays. Chest X-ray images can identify lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). Because many illnesses share similar patterns and diagnoses, radiologists and clinicians find it challenging to distinguish between them. This study uses convolutional neural networks (CNN) and ensemble models (VGG-19, ResNet50, vision transformer) to analyze chest X-ray images from internet sources to identify lung illness. The present research proposed an ensemble model comprised of VGG-19, ResNet50, and a vision transformer for the detection of COVID-19 and normal images with an accuracy of 92.46 percent and 94.52 % for the detection of COVID-19, normal and viral pneumonia respectively. Also, a comparative study has been conducted between CNN and ensemble approach for detecting COVID-19 and pneumonia disease from chest x-ray images. The proposed approach may also help in the early detection of other variants of COVID-19 such as JN.1, and HV.1 in the future.

Application of Ensemble Learning in CXR Classification for Enhancing COVID-19 Diagnosis

This study delves into the vital task of classifying chest X-ray (CXR) samples, particularly those related to respiratory ailments, using advanced clinical image analysis and computer-aided radiology techniques. Its primary focus is on developing a classifier to accurately identify COVID-19 cases. Through the application of machine learning and computer vision methodologies, the research aims to enhance the precision of COVID-19 detection. It investigates the effectiveness of Histogram of Oriented Gradients (HOG) feature extraction techniques in conjunction with various classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), K-nearest neighbor (KNN), and Tree Bagger (TB), alongside an innovative ensemble learning approach. Results indicate impressive accuracy rates, with KNN, SVM, DT, NB, and TB all surpassing the 90% mark. However, the ensemble learning method emerges as the standout performer. By leveraging HOG features extracted from CXR images, this approach presents a robust solution for COVID-19 diagnosis, offering a powerful tool to address the diagnostic challenges posed by the pandemic.

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.

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.

Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image Classification

Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional neural networks (CNNs) such as VGG16, ResNet 50, and DenseNet 121, modifying their output layers and fine-tuning the models. We employ a novel optimization strategy using the Hyperband algorithm to efficiently search the hyperparameter space while adjusting the fully connected layers of the CNNs. The effectiveness of our approach is evaluated on the basis of the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metric. Our proposed methodology could assist in automated chest radiograph interpretation, offering a valuable tool that can be used by clinicians in the future.

Chest X-ray image classification using transfer learning and hyperparameter customization for lung disease diagnosis

ABSTRACT Lung diseases often result in severe damage to the respiratory tract, and lead to a high risk of mortality within a short period of time. DL models based on ViT are considered to have promising advantages over CNN architectures in terms of computational efficiency, and accuracy when trained on large ImageNet datasets. In this study, we present a new DL approach based on the combination of CNN with ViT to improve the efficiency of pneumonia diagnosis using medical images. In the first stage, raw images are passed through a local filter to capture local relations on the inputs. The local filter block includes two convolutional layers with kernel 3 × 3. This local filtering method aims to enhance rich features before being fed into the patching layer of the ViT block. The proposed method is experimented on the benchmark chest X-ray dataset. The proposed method is evaluated and compared to some well-known models, which include ViT, VGG19, Resnet50, Densnet201. Experimental results demonstrated that the proposed approach based on CNN and ViT reaches higher efficiency with about 1% accuracy to the standard ViT model, and about 2% higher with VGG19, Resnet50, Densnet201 and smaller in model architecture.

Pneumonia Detection In Chest X-Rays Using Neural Networks

Deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images for Radiological Society of North America Pneumonia (RSNA) datasets. The study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The proposed method is based on a noncomplex CNN and the use of transfer learning algorithms like Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The RSNA benchmark MAP score is 0.25 but using the Mask RCNN model (Region Convolutional Neural Network) on a stratified sample of 3017 along with image augmentation gave a MAP (Mean Average Precision) score of 0.15. Meanwhile, the YoloV3 without any hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps decreasing. Running the model for a greater number of iterations can give better results. Pneumonia is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of Pneumonia is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (Xray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Keywords: X-Rays, Deep learning techniques, CNN model, RSNA.

Domain adaptation via Wasserstein distance and discrepancy metric for chest X-ray image classification

Deep learning technology can effectively assist physicians in diagnosing chest radiographs. Conventional domain adaptation methods suffer from inaccurate lesion region localization, large errors in feature extraction, and a large number of model parameters. To address these problems, we propose a novel domain-adaptive method WDDM to achieve abnormal identification of chest radiographic images by combining Wasserstein distance and difference measures. Specifically, our method uses BiFormer as a multi-scale feature extractor to extract deep feature representations of data samples, which focuses more on discriminant features than convolutional neural networks and Swin Transformer. In addition, based on the loss minimization of Wasserstein distance and contrast domain differences, the source domain samples closest to the target domain are selected to achieve similarity and dissimilarity across domains. Experimental results show that compared with the non-transfer method that directly uses the network trained in the source domain to classify the target domain, our method has an average AUC increase of 14.8% and above. In short, our method achieves higher classification accuracy and better generalization performance.