Chest X-ray Image Classification Research Articles

Retracted: Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.

[This retracts the article DOI: 10.1155/2022/6185013.].

Handling severe data imbalance in chest X-Ray image classification with transfer learning using SwAV self-supervised pre-training

Ever since the COVID-19 outbreak, numerous researchers have attempted to train accurate Deep Learning (DL) models, especially Convolutional Neural Networks (CNN), to assist medical personnel in diagnosing COVID-19 infections from Chest X-Ray (CXR) images. However, data imbalance and small dataset sizes have been an issue in training DL models for medical image classification tasks. On the other hand, most researchers focused on complex novel methods instead and few explored this problem. In this research, we demonstrated how Self-Supervised Learning (SSL) can assist DL models during pre-training, and Transfer Learning (TL) can be used in training the models, which can produce models that are more robust to data imbalance. The Swapping Assignment between Views (SwAV) algorithm in particular has been known to be outstanding in enhancing the accuracy of CNN models for classification tasks after TL. By training a ResNet-50 model pre-trained using SwAV on a severely imbalanced CXR dataset, the model managed to greatly outperform its counterpart pre-trained in a standard supervised manner. The SwAV-TL ResNet-50 model attained 0.952 AUROC with 0.821 macro-averaged F1 score when trained on the imbalanced dataset. Hence, it was proven that TL using models pre-trained through SwAV can achieve better accuracy even when the dataset is severely imbalanced, which is usually the case in medical image datasets.

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attention-aware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attention-aware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F- score of 0.92, indicating that the suggested model outperformed against the baseline models.

Optimized deterministic multikernel extreme learning machine for classification of COVID-19 chest Xray images

In this paper, a novel technique has been proposed to exploit the capability of residual network (ResNet) deep learning model to extract the features. It is utilized neither in pretrained form nor as a transfer learning model. ResNet uses shortcut connections to create shortcut blocks in order to skip blocks of convolutional layers (residual blocks). These stacked residual blocks significantly increase training effectiveness and address the degradation issue. For the purpose of classification, a multiple kernel learning based deterministic extreme learning machine (MKD-ELM) which uses a linear combination of different base kernels as target kernel function is designed to classify chest Xray images. Multiple kernels are used here to exploit their non-linear mapping capability on heterogeneous data. MKD-ELM is an enhanced classifier, which does not require iterative training of its parameters. The proposed technique has better feature extraction along with non-iterative training, thus it is having very fast training and very good generalization performance. The kernel and regularization parameters that influence how accurate MKD-ELM is at classifying data, are tuned through experimentation. So, an optimization technique called the genetic algorithm (GA) has been utilized to determine the ideal combination of these parameters for improved performance. The performance of the proposed technique is analysed for COVID-19 detection problem using chest Xray (ChXR) images by changing the training set, types of kernels and coefficients used for combining base kernels. The proposed algorithm achieves a 97.27% recognition rate on first dataset which comprises 5,856 images and 99.06% on the second dataset which consists of 13,808 images. A higher recognition rate is attained for these ChXR image datasets, in respect to modern techniques demonstrating the effectiveness of the proposed algorithm.

Experiments with Dimensionality Reduction on Chest X-Ray Images

In this work, we study the problem of chest X-ray image classification. We use well-known convolutional neural network architectures (ResNet18, ResNet50, VGG19, MobileNet, ShuffleNet, and EfficientNetB0) trained on chest X-ray images, to extract feature vectors for the images in the dataset. The classification process is mainly performed with Support Vector Machines (SVM) and Random Forest (RF) methods. The central focus of this work is to study the influence of dimensionality reduction on the classification results. First, we used Principal Component Analysis (PCA) and reduced the length of the feature vectors. Secondly, we perform a selection process of the images in each class, from the training set. This selection procedure aims to preserve in the training set the images that provide better characteristics of the class they belong to. The selection process uses clustering methods (k-means, hierarchical clustering, Self-Organizing Maps). For the numerical experiments we used RSNA (Radiological Society of North America) dataset with images divided into three classes. The best results were obtained using Random Forest classification method applied on features extracted with ResNet50 and by using a reduction of the dataset based on Ward agglomerative clustering technique.

A Novel COVID-19 Image Classification Method Based on the Improved Residual Network

In recent years, chest X-ray (CXR) imaging has become one of the significant tools to assist in the diagnosis and treatment of novel coronavirus pneumonia. However, CXR images have complex-shaped and changing lesion areas, which makes it difficult to identify novel coronavirus pneumonia from the images. To address this problem, a new deep learning network model (BoT-ViTNet) for automatic classification is designed in this study, which is constructed on the basis of ResNet50. First, we introduce multi-headed self-attention (MSA) to the last Bottleneck block of the first three stages in the ResNet50 to enhance the ability to model global information. Then, to further enhance the feature expression performance and the correlation between features, the TRT-ViT blocks, consisting of Transformer and Bottleneck, are used in the final stage of ResNet50, which improves the recognition of complex lesion regions in CXR images. Finally, the extracted features are delivered to the global average pooling layer for global spatial information integration in a concatenated way and used for classification. Experiments conducted on the COVID-19 Radiography database show that the classification accuracy, precision, sensitivity, specificity, and F1-score of the BoT-ViTNet model is 98.91%, 97.80%, 98.76%, 99.13%, and 98.27%, respectively, which outperforms other classification models. The experimental results show that our model can classify CXR images better.

DETECTION OF COVID-19 WITH CHEST X-RAY

In order to speed up finding of causes of COVID-19 illness, this study developed novel diagnostic platform using profound convolutional neural network (CNN) helping radiologists diagnose COVID-19 pneumonia beside non-COVID-19 pneumonia in patient in Middle more Hospital. As the name suggests, crucial objective of our research is to produce a chest X-ray image classification program which could properly identify a scan's categorization as either "normal," "viral pneumonia," or "COVID-19." Using X-rays, we will train an image classifier to determine whether or not a person has COVID-19. In this data set, there are over 3000 chest X-ray pictures categorized in normal, viral, as well as COVID-19. A picture classifying system which properly identifies which of three categories Chest X-Ray scan corresponds with is purpose of this investigation.

Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images

In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.

Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images.

Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., "COVID-19", "Pneumonia", and "Normal". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.

Pneumonia Detection Using Novel Deep Learning Techniques

Pneumonia is one of the leading infectious disease that can kill children and elderly people around the world. The development of an automated system to detect pneumonia would be advantageous, especially to enable treatment of this disease in remote areas without much delay. pneumonia is an lung infectious disease which mainly affects the small air sacs known as alveoli. The main symptoms of pneumonia includes cough, fever and breathing problems . Aged people, children and persons who have medical problems are the main victims of this disease. Around 450 million people are affected by this disease on an average of each and every year. The most commonly used technique for diagnosing this disease is chest X-ray imaging. Chest X-ray examination is complex procedure to detect the disease because it involves lots of vulnerabilities. With latest advances in technologies we can use deep learning algorithms to detect the disease using chest x ray images. To deal with the scarcity of data, we used Deep Transfer Learning and designed the Hybrid Algorithms. The images of the chest X-rays were fed into the individual algorithms for training purposes. Parallel Deep Feature Extractors are used in conjunction with various algorithms. For classifying chest X-ray images into normal and pneumonia, Here we are proposing an hybrid model based on VGG16, VGG19, CNN, and Mobile Net networks. Individual image classification algorithms were combined to form a hybrid model .In comparison to individual algorithms, the new Hybrid Model with Deep Learning achieved higher accuracy than existing methods.

Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network

A Convolutional Neural Network (CNN) classifier is generally utilized to classify an image tensor according to the mapped labels. The simplification of the classifier causes CNN to be often used to classify images, especially in the biomedical field. Thus, CNN is widely used to classify computer tomography (CT) and chest X-ray (CXR) images against the mapped labels. Several transfer learning models were implemented to classify CXR images for preliminary detection of COVID-19 infection, e.g., ResNet, Inception, Xception, etc. However, a transfer learning model has a maximum and minimum input resolution. Thus, the computational cost tends to be huge and unable to be optimized. Therefore, A custom CNN model can be a solution to reduce computational costs by configuring the feature extraction layers. This study proposed an efficient reduction of feature extraction for COVID-19 CXR namely Depthwise Separable Convolution Network. Furthermore, numerous strategies were adopted to lower the computational cost while retaining accuracy, including customizing the Batch Normalization (BN) layer and replacing the convolution layer with a separable convolution layer. The proposed model successfully reduced the feature extraction represented by the decreases in trainable parameters from 28.640 trainable parameters to 4.640 trainable parameters. The depthwise separable convolution effectively retains the performance accuracy 72.96%, loss 12.43%, recall 74.67%, precision 77.67%, and F1-score 75.33%. The CXR augmentation is also successfully increase the performance accuracy 74.55%, loss 11.37%, recall 77.67%, precision 79.56%, and F1-score 78.33%.

Chest X-ray Image Classification for COVID-19 diagnoses

Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed to advance the detection and identify vector features of X-ray images with a quality that is too poor to be read by radiologists. Deep learning is an intelligent system that can be used in this case. Objective: The current study compares the classification and accuracy of detection methods with two, three dan five classes. Methods: Deep learning can classify visual geometry group VGG 19 architectures with 1000 classes. The classification of the five classes' convolutional neural network (CNN) underwent model validation with a confusion matrix to produce accuracy and class values. The system could then diagnose patients’ examinations by radiology specialists. Results: The results of the five-class method showed 98% accuracy, the three-class method showed 99.99%, and the two-class showed 99.99%. Conclusion: It can be concluded that using the VGG 19 model is effective. This system can classify and diagnose viruses in patients to assist radiologists by reading the images. Keywords: COVID-19, CNN, Classification, Deep Learning

COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning

The infectious coronavirus disease-19 (COVID-19) is a viral disease that affects the lungs, which caused great havoc when the epidemic rapidly spread around the world. Polymerase chain reaction (PCR) tests are conducted to screen for COVID-19 and respond to quarantine measures. However, PCR tests take a considerable amount of time to confirm the test results. Therefore, to supplement the accuracy and quickness of a COVID-19 diagnosis, we proposed an effective deep learning methodology as a quarantine response through COVID-19 chest X-ray image classification based on domain extension transfer learning. As part of the data preprocessing, contrast limited adaptive histogram equalization was applied to chest X-ray images using Medical Information Mart for Intensive Care (MIMIC)-IV obtained from the Beth Israel Deaconess Medical Center. The classification of the COVID-19 X-ray images was conducted using a pretrained ResNet-50. We also visualized and interpreted the classification performance of the model through explainable artificial intelligence and performed statistical tests to validate the reliability of the model. The proposed method correctly classified images with 96.7% accuracy, an improvement of about 9.9% over the reference model. This study is expected to help medical staff make an integrated decision in selecting the first confirmed case and contribute to suppressing the spread of the virus in the community.

Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning

BackgroundNowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it’s very important to be accurate in the early stages of diagnosis and treatment.ResultsWe applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology’s. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble.ConclusionsTo minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.

IEViT: An enhanced vision transformer architecture for chest X-ray image classification

Background and Objective: Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert radiologists to view and interpret chest X-ray images can be a bottleneck, especially in remote and deprived areas. Recent advances in machine learning have made possible the automated diagnosis of chest X-ray scans. In this work, we examine the use of a novel Transformer-based deep learning model for the task of chest X-ray image classification.Methods: We first examine the performance of the Vision Transformer (ViT) state-of-the-art image classification machine learning model for the task of chest X-ray image classification, and then propose and evaluate the Input Enhanced Vision Transformer (IEViT), a novel enhanced Vision Transformer model that can achieve improved performance on chest X-ray images associated with various pathologies.Results: Experiments on four chest X-ray image data sets containing various pathologies (tuberculosis, pneumonia, COVID-19) demonstrated that the proposed IEViT model outperformed ViT for all the data sets and variants examined, achieving an F1-score between 96.39% and 100%, and an improvement over ViT of up to +5.82% in terms of F1-score across the four examined data sets. IEViT’s maximum sensitivity (recall) ranged between 93.50% and 100% across the four data sets, with an improvement over ViT of up to +3%, whereas IEViT’s maximum precision ranged between 97.96% and 100% across the four data sets, with an improvement over ViT of up to +6.41%.Conclusions: Results showed that the proposed IEViT model outperformed all ViT’s variants for all the examined chest X-ray image data sets, demonstrating its superiority and generalisation ability. Given the relatively low cost and the widespread accessibility of chest X-ray imaging, the use of the proposed IEViT model can potentially offer a powerful, but relatively cheap and accessible method for assisting diagnosis using chest X-ray images.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.

CheXGAT: A disease correlation-aware network for thorax disease diagnosis from chest X-ray images

Chest X-ray (CXR) imaging is one of the most common diagnostic imaging techniques in clinical diagnosis and is usually used for radiological examinations to screen for thorax diseases. In this paper, we propose a novel computer-aided diagnosis (CAD) system based on a hybrid deep learning model composed of a convolutional neural network (CNN) and a graph neural network (GNN). The system is intended to explore implicit correlations between thorax diseases to aid in the multilabel chest X-ray image classification task, which we term ‶CheXGAT‶. Specifically, the proposed CheXGAT framework comprises two main modules: an image representation learning (IRL) module and a graph representation learning (GRL) module. We employ the IRL module to learn high-level visual representation features from the CXR image. From the GRL module, the self-attention mechanism aggregates neighborhood features from the graphic structure to enhance the implicit correlation between thorax diseases. We adopted a data-driven method to create a disease correlation matrix that works on the message passing and aggregation process for the nodes in the GRL module. After end-to-end training, the GRL module enhances the correlation between thorax diseases to improve diagnosis performance. We performed experiments on the NIH Chest X-ray14 dataset, which contains 112,120 frontal-view radiographs; each image has multiple thorax disease labels. In the experimental results, the average AUC score of our proposed CheXGAT model reached 0.8266, and the AUC scores of Emphysema and Hernia reached 0.9447 and 0.9313, respectively. In addition, we visualized explanations via a gradient-based localization method in our proposed CheXGAT framework. Compared with previous studies, the experimental results show the competitive performance of our framework. Specifically, we propose a CAD system that uses a hybrid model to help radiologists identify CXR images from 14 chest diseases for clinical diagnosis.

Data-Efficient Training of Pure Vision Transformers for the Task of Chest X-ray Abnormality Detection Using Knowledge Distillation.

It is generally believed that vision transformers (ViTs) require a huge amount of data to generalize well, which limits their adoption. The introduction of data-efficient algorithms such as data-efficient image transformers (DeiT) provided an opportunity to explore the application of ViTs in medical imaging, where data scarcity is a limiting factor. In this work, we investigated the possibility of using pure transformers for the task of chest x-ray abnormality detection on a small dataset. Our proposed framework is built on a DeiT structure benefiting from a teacher-student scheme for training, with a DenseNet with strong classification performance as the teacher and an adapted ViT as the student. The results show that the performance of transformers is on par with that of convolutional neural networks (CNNs). We achieved a test accuracy of 92.2% for the task of classifying chest x-ray images (normal/pneumonia/COVID-19) on a carefully selected dataset using pure transformers. The results show the capability of transformers to accompany or replace CNNs for achieving state-of-the-art in medical imaging applications. The code and models of this work are available at https://github.com/Ouantimb-Lab/DeiTCovid.

Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification.

The diagnosis of chest diseases is a challenging task for assessing thousands of radiology subjects. Their diagnosis decisions heavily rely on the expert radiologists' manual annotations. It is important to develop automated analysis methods for the computer-aided diagnosis of chest diseases on chest radiography. To explore the label relationship and improve the diagnosis performance, we present an end-to-end multi-label learning framework for jointly modeling the global and local label correlation, called GL-MLL that (1) explores the label correlation from a globally static view and a locally adaptive view, (2) considers the imbalanced class distribution, and (3) focuses on capturing label-specific features in image-level representation. We validate the performance of the proposed framework on the CheXpert dataset. The results demonstrate that the proposed GL-MLL outperforms state-of-the-art approaches. The code is available athttps://github.com/llt1836/GL-MLL.

An analysis of chest x-ray image classification and identification during COVID-19 based on deep learning models

An analysis of chest x-ray image classification and identification during COVID-19 based on deep learning models