Classification Of X-ray Images Research Articles

Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration

This research presents an integrated framework designed to automate the classification of pulmonary chest x-ray images. Leveraging convolutional neural networks (CNNs) with a focus on transformer architectures, the aim is to improve both the accuracy and efficiency of pulmonary chest x-ray image analysis. A central aspect of this approach involves utilizing pre-trained networks such as VGG16, ResNet50, and MobileNetV2 to create a feature ensemble. A notable innovation is the adoption of a stacked ensemble technique, which combines outputs from multiple pre-trained models to generate a comprehensive feature representation. In the feature ensemble approach, each image undergoes individual processing through the three pre-trained networks, and pooled images are extracted just before the flatten layer of each model. Consequently, three pooled images in 2D grayscale format are obtained for each original image. These pooled images serve as samples for creating 3D images resembling RGB images through stacking, intended for classifier input in subsequent analysis stages. By incorporating stacked pooling layers to facilitate feature ensemble, a broader range of features is utilized while effectively managing complexities associated with processing the augmented feature pool. Moreover, the study incorporates the Swin Transformer architecture, known for effectively capturing both local and global features. The Swin Transformer architecture is further optimized using the artificial hummingbird algorithm (AHA). By fine-tuning hyperparameters such as patch size, multi-layer perceptron (MLP) ratio, and channel numbers, the AHA optimization technique aims to maximize classification accuracy. The proposed integrated framework, featuring the AHA-optimized Swin Transformer classifier utilizing stacked features, is evaluated using three diverse chest x-ray datasets-VinDr-CXR, PediCXR, and MIMIC-CXR. The observed accuracies of 98.874%, 98.528%, and 98.958% respectively, underscore the robustness and generalizability of the developed model across various clinical scenarios and imaging conditions.

Comparative analysis of X-ray image classification of pneumonia based on deep learning algorithm

Abstract. In this paper, we compare and analyse the effectiveness of deep learning models such as AlexNet, Vgg, GoogleNet and MobileNet in the task of pneumonia image classification. During the training process, GoogleNet showed the fastest convergence speed, reaching convergence at the 3rd epoch; subsequently, the other three models gradually stabilised. In the end, the loss of AlexNet is 0.426, the loss of Vgg is 0.566, the loss of GoogleNet is 0.936, and the loss of MobileNet is 0.626. By selecting the weights of the round with the best training effect of each model, the results of classification accuracy are obtained: 88.9% for AlexNet, 92.6% for Vgg, and 92.6% for GoogleNet. 92.6%, GoogleNet is 85.2%, and MobileNet is 96.3%. MobileNet demonstrated the best prediction performance on the test set. These results provide a useful reference for the application of deep learning models in medical imaging..

Comparative Analysis of Neural Network Architectures for Automated Fracture Detection in Hand X-ray Images

The application of several neural network architectures—including Fully Connected Networks (FCN), Convolutional Neural Networks (CNN), pretrained ResNet, Vision Transformer (ViT-B-16)—for the classification of hand X-ray images into "Fractured" and "Not Fractured"—categories is investigated in this work. The main goals are to evaluate these models' fracture detection ability and determine which architectural design fits this work. Because transfer learning let the model use past information from big-scale picture datasets, the pretrained ResNet model emerged as the most effective with high accuracy, stability, and resilience. The bespoke CNN also performed well, displaying excellent feature extraction powers especially for medical imaging. But the non-pretrained ResNet model overfitted, meaning deeper networks find it difficult to generalize without pretraining. Though innovative, the Vision Transformer performed poorly since it depends on a lot of training data and finds difficult learning of intricate spatial properties from little datasets. Although acting as a baseline, the FCN's simple architecture and incapacity to detect spatial hierarchies in images meant it could not match the efficacy of CNN models. Emphasizing the important function of transfer learning in clinical applications, the results show that pretrained CNN architectures, especially ResNet, offer the most consistent and accurate method for automatic fracture diagnosis in medical pictures.

A Robust Hybrid Machine and Deep Learning-based Model for Classification and Identification of Chest X-ray Images

A Robust Hybrid Machine and Deep Learning-based Model for Classification and Identification of Chest X-ray Images

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.

Knee Osteoporosis Diagnosis Based on Deep Learning

Osteoporosis, a silent yet debilitating disease, presents a significant challenge due to its asymptomatic nature until fractures occur. Rapid bone loss outpaces regeneration, leading to pain, disability, and loss of independence. Early detection is pivotal for effective management and fracture risk reduction, yet current diagnostic methods are time-consuming. Despite its importance, research addressing early diagnosis remains limited. Deep learning, particularly convolutional neural networks (CNNs), has emerged as a potent tool in image analysis. This paper presents a novel approach utilizing transfer learning with CNNs for osteoporosis detection from X-ray images. The proposed approach not only achieves a high accuracy of osteoporosis diagnosis but also offers a revealed feature map that can guide medical professionals for osteoporosis diagnosis. The innovation lies in a dual strategy: (i) a model integrating transfer learning from CNN architectures such as AlexNet, VGG-16, ResNet-50, VGG-19, InceptionNet, XceptionNet, and a custom CNN, and (ii) a dataset collection augmentation mechanism to enhance learning accuracy. The study includes binary and multiclass classification of knee joint X-ray images into normal, osteopenia, and osteoporosis groups, utilizing a dataset of 1947 knee X-rays for training and testing. Performance comparisons against state-of-the-art models reveal the proposed VGG-19 model achieves the highest accuracy at 92.0% for multiclass and 97.5% for binary. These findings underscore the potential of deep learning with transfer learning in aiding early osteoporosis detection, thereby mitigating fracture risks.

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.

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

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

TO ANALYZE THE LUNGS X-RAY IMAGES USING MACHINE LEARNING ALGORITHM: AN IMPLEMENTATION TO PNEUMONIA DIAGNOSIS

Introduction: Respiratory diseases, particularly pneumonia, pose a significant threat to human life. Pneumonia affects the respiratory function in the human body and is a dangerous lung disease. This study aims to propose a model for detecting pneumonia in chest XR images. By utilizing statistical-based features, relevant and informative features are extracted from lung X-ray images. Objective: The objective is to obtain high accuracy in pneumonia identification; the target of this work is to generate a model that can precisely recognize the presence of pneumonia by evaluating chest X-ray pictures. Method: The Method follows a three-phase approach: preprocessing, categorization, and extraction of features. Preprocessing is the stage when various filters are applied to the chest X-ray images to enhance their eminence and eradicate noise. The feature extraction phase involves extracting statistical-based features from the preprocessed images. These features capture relevant information regarding a pneumonia diagnosis. Finally, in the classification phase, algorithms for machine learning are employed to use the retrieved features to categorize the X-ray pictures as infected or uninfected. Result: The proposed model successfully detects the presence of pneumonia accurately. By leveraging advanced machine learning algorithms, the model achieves accurate X-ray image classification for the chest. Conclusion: This study concludes by presenting a model for detecting pneumonia by examining chest X-ray pictures. To accurately classify infected and non-infected lungs, the proposed model makes use of image dispensation methods and machine learning algorithms. The model's high accuracy in pneumonia detection can significantly contribute to early diagnosis and treatment.

Identification of Spinal Disorders Based on Spine X-ray Dataset Processing Using the LBP and CNN Algorithms

This research will use deep learning in conducting spinal x-ray image analysis but computational time problems are a problem of this study. Computations on deep learning across multiple nodes can increase training time and longer computation time compared to machine learning models. Based on experimental results, the best spine x-ray image classification results when using the CNN model with accuracy at the training stage, evaluation stage and test stage were 69.00%, 83.33% and 81.16% respectively. CNN models optimized with LBP get the lowest accuracy, with results at the training stage of 62.64%, validation stage of 75.00% and testing stage of 65.22%. LBP feature extraction turns out to have several drawbacks when combined with the CNN model, one major drawback is its inability to process global spatial information while retaining local texture information which causes LBP to be unable to capture the entire structure or context of the image, focusing only on local patterns so that many features of the image are lost. Another issue is the sensitivity of CNNs to image data, which can affect classification accuracy.

Enhancing Cardiovascular Disease Detection through Deep Multimodal Fusion: Integrating Radiology and Electrocardiogram via Convolutional Neural Network

Cardiovascular Diseases (CVDs), also known as heart diseases are related to a process of atherosclerosis, which is when a plaque builds up in the arteries and blocks blood flow. CVDs are an ever-increasing health risk for the general population, and according to the American Heart Association, the number of deaths related to CVDs has surpassed the previous 910,000 all-time record from 2003. To solve this problem, numerous novel approaches have been presented for a solution to decrease such anomalies with traditional methods utilizing computed tomography scans (CT scans) to calculate coronary artery calcium. However, countless problems arise from using CT scans as CT scans are not accessible to the general public, and early diagnosis is especially difficult. So, this research paper aims to explore the potential of AI-driven biomarkers in enhancing the efficiency of CVD diagnosis. Moreover, this study presents a novel approach that uses artificial intelligence to enhance the accuracy of CVD diagnosis. Specifically, this paper suggests an innovative methodology that utilizes the classification of x-ray images that is additionally supplemented by classifying electrocardiograms. These techniques allow for analysis of multi-modal data sources allowing latent information on CVDs to be extracted. Throughout the experiments, the proposed method has proven that it is superior compared to other state-of-the-art methods, as it has achieved an accuracy of 90.49%.

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

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.