Chest X-ray Classification Research Articles

Tuberculosis Detection Using Convolutional Neural Network

Tuberculosis (TB) is a chronic lung disease caused by bacterial infection and remains one of the leading causes of mortality worldwide. Early and accurate detection of TB is crucial for effective treatment and prevention of complications. In this study, we propose a deep learning-based system for TB detection using chest X-ray images. The system leverages the VGG16 architecture for classification, supported by image preprocessing and data augmentation to improve model performance. The dataset comprises X-ray images categorized into TB-infected and normal cases. The VGG16 model was trained, validated, and tested to classify chest X-rays, achieving a train accuracy of 98.55%, validation accuracy of 99.75% and test accuracy of 98.73%, for TB detection. Our results demonstrate the effectiveness of the VGG16-based model in reliably detecting TB from chest X-rays while providing practical healthcare accessibility through the user interface. This combined approach can serve as a valuable tool in computer-aided TB diagnosis, enabling timely and accurate detection to improve patient outcomes. Key Words: Chest X-ray Classification, Deep Learning, Medical Image Analysis, Computer-Aided Diagnosis (CAD), Transfer Learning, TB Diagnosis.

Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs

Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs

Diagnosing Respiratory Variability: Convolutional Neural Networks for Chest X-ray Classification Across Diverse Pulmonary Conditions.

The global burden of lung diseases is a pressing issue, particularly in developing nations with limited healthcare access. Accurate diagnosis of lung conditions is crucial for effective treatment, but diagnosing lung ailments using medical imaging techniques like chest radiograph images and CT scans is challenging due to the complex anatomical intricacies of the lungs. Deep learning methods, particularly convolutional neural networks (CNN), offer promising solutions for automated disease classification using imaging data. This research has the potential to significantly improve healthcare access in developing countries with limited medical resources, providing hope for better diagnosis and treatment of lung diseases. The study employed a diverse range of CNN models for training, including a baseline model and transfer learning models such as VGG16, VGG19, InceptionV3, and ResNet50. The models were trained using image datasets sourced from the NIH and COVID-19 repositories containing 8000 chest radiograph images depicting four lung conditions (lung opacity, COVID-19, pneumonia, and pneumothorax) and 2000 healthy chest radiograph images, with a ten-fold cross-validation approach. The VGG19-based model outperformed the baseline model in diagnosing lung diseases with an average accuracy of 0.995 and 0.996 on validation and external test datasets. The proposed model also outperformed published lung-disease prediction models; these findings underscore the superior performance of the VGG19 model compared to other architectures in accurately classifying and detecting lung diseases from chest radiograph images. This study highlights AI's potential, especially CNNs like VGG19, in improving diagnostic accuracy for lung disorders, promising better healthcare outcomes. The predictive model is available on GitHub at https://github.com/PGlab-NIPER/Lung_disease_classification .

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning

The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the chest X-ray images (CXR) with the deep convolutional neural network (DCNN) models through a fine-tuning and pre-training approach. CXR images pertaining to four health scenarios, namely, healthy, COVID-19, bacterial pneumonia and viral pneumonia, are considered and subjected to data augmentation. Two types of input datasets are prepared; in which dataset I contains the original image dataset categorised under four classes, whereas the original CXR images are subjected to image pre-processing via Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm and Blackhat Morphological Operation (BMO) for devising the input dataset II. Both datasets are supplied as input to various DCNN models such as DenseNet, MobileNet, ResNet, VGG16, and Xception for achieving multi-class classification. It is observed that the classification accuracies are improved, and the classification errors are reduced with the image pre-processing. Overall, the VGG16 model resulted in better classification accuracies and reduced classification errors while accomplishing multi-class classification. Thus, the proposed work would assist the clinical diagnosis, and reduce the workload of the front-line healthcare workforce and medical professionals.

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.

Approaches and processes for paediatric chest X-ray classification used in the SHINE TB treatment-shortening trial.

<sec><title>INTRODUCTION</title>SHINE (Shorter Treatment for Minimal Tuberculosis in Children) was the first Phase 3 paediatric TB treatment-shortening trial. Robust chest X-ray (CXR) classification methods were integral to excluding severe disease for trial eligibility and to retrospectively adjudicating TB status at baseline. We describe and critically evaluate the CXR classification approaches and processes used in the SHINE trial.</sec><sec><title>METHODS</title>Children with non-severe TB were randomised to 4- vs 6-months anti-TB treatment. Radiologically non-severe TB was defined on CXR. CXRs were systematically interpreted by on-site clinicians prospectively for eligibility determination and retrospectively by experts to inform adjudication of baseline TB status and disease severity.</sec><sec><title>RESULTS</title>A screening CXR was successfully obtained from all 1,204 enrolled children; 1,134 CXRs from children with intra-thoracic TB were reviewed by expert readers. Compared with the expert panel, enrolling clinicians classified more CXRs as abnormal and 'typical TB' and all as radiologically non-severe. The expert panel retrospectively classified 71/1,134 (6%) CXRs as severe. Of these, 4 (5.6%) had unfavourable outcomes compared with 34 (3.0%) in the trial overall.</sec><sec><title>DISCUSSION</title>Using CXRs to classify radiological disease severity and inform eligibility decisions in real-time by local enrolling clinicians was feasible and safe in this large paediatric TB trial. Retrospective central expert CXR review was successful. Refinement of the CXR methods for the classification of both disease severity and TB status could support standardised implementation in routine care and research.</sec>.

Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates

BackgroundChest radiographs (CXRs) are widely used to screen for infectious diseases like tuberculosis and COVID-19 among migrants. At such high-volume settings, manual CXR reporting is challenging and integrating artificial intelligence (AI) algorithms into the workflow help to rule out normal findings in minutes, allowing radiologists to focus on abnormal cases. MethodsIn this post-deployment study, all the CXRs acquired during the visa screening process across 33 centers in United Arab Emirates from January 2021 to June 2022 (18 months) were included. The qXR v2.1 chest X-ray interpretation software was used to classify the scans into normal and abnormal, and its agreement against radiologist was evaluated. Additionally, a digital survey was conducted among 20 healthcare professionals with prior AI experience to understand real-world implementation challenges and impact. ResultsThe analysis of 1309,443 CXRs from 1309,431 patients (median age: 35 years; IQR [29–42]; 1030,071 males [78.7 %]) in this study revealed a Negative Predictive Value (NPV) of 99.92 % (95 % CI: 99.92, 99.93), Positive Predictive Value (PPV) of 5.06 % (95 % CI: 4.99, 5.13) and overall percent agreement of the AI with radiologists of 72.90 % (95 % CI: 72.82, 72.98). In the survey, majority (88.2 %) of the radiologists agreed to turnaround time reduction after AI integration, while 82 % suggested that the AI improved their diagnostic accuracy. DiscussionIn contrast with the existing studies, this research uses a substantially large data. A high NPV and satisfactory agreement with human readers indicate that AI can reliably identify normal CXRs, making it suitable for routine applications.

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.

Efficient federated learning for pediatric pneumonia on chest X-ray classification

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia.

A systematic review of multilabel chest X-ray classification using deep learning

A systematic review of multilabel chest X-ray classification using deep learning

Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images.

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network's determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm.

UniChest: Conquer-and-Divide Pre-Training for Multi-Source Chest X-Ray Classification.

Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs). However, current works mainly pay attention to the exploration on single dataset of CXRs, which locks the potential of this powerful paradigm on larger hybrid of multi-source CXRs datasets. We identify that although blending samples from the diverse sources offers the advantages to improve the model generalization, it is still challenging to maintain the consistent superiority for the task of each source due to the existing heterogeneity among sources. To handle this dilemma, we design a Conquer-and-Divide pre-training framework, termed as UniChest, aiming to make full use of the collaboration benefit of multiple sources of CXRs while reducing the negative influence of the source heterogeneity. Specially, the "Conquer" stage in UniChest encourages the model to sufficiently capture multi-source common patterns, and the "Divide" stage helps squeeze personalized patterns into different small experts (query networks). We conduct thorough experiments on many benchmarks, e.g., ChestX-ray14, CheXpert, Vindr-CXR, Shenzhen, Open-I and SIIM-ACR Pneumothorax, verifying the effectiveness of UniChest over a range of baselines, and release our codes and pre-training models at https://github.com/Elfenreigen/UniChest.

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.

Language model-based labeling of German thoracic radiology reports.

The aim of this study was to explore the potential of weak supervision in a deep learning-based label prediction model. The goal was to use this model to extract labels from German free-text thoracic radiology reports on chest X-ray images and for training chest X-ray classification models.The proposed label extraction model for German thoracic radiology reports uses a German BERT encoder as a backbone and classifies a report based on the CheXpert labels. For investigating the efficient use of manually annotated data, the model was trained using manual annotations, weak rule-based labels, and both. Rule-based labels were extracted from 66071 retrospectively collected radiology reports from 2017-2021 (DS 0), and 1091 reports from 2020-2021 (DS 1) were manually labeled according to the CheXpert classes. Label extraction performance was evaluated with respect to mention extraction, negation detection, and uncertainty detection by measuring F1 scores. The influence of the label extraction method on chest X-ray classification was evaluated on a pneumothorax data set (DS 2) containing 6434 chest radiographs with associated reports and expert diagnoses of pneumothorax. For this, DenseNet-121 models trained on manual annotations, rule-based and deep learning-based label predictions, and publicly available data were compared.The proposed deep learning-based labeler (DL) performed on average considerably stronger than the rule-based labeler (RB) for all three tasks on DS 1 with F1 scores of 0.938 vs. 0.844 for mention extraction, 0.891 vs. 0.821 for negation detection, and 0.624 vs. 0.518 for uncertainty detection. Pre-training on DS 0 and fine-tuning on DS 1 performed better than only training on either DS 0 or DS 1. Chest X-ray pneumothorax classification results (DS 2) were highest when trained with DL labels with an area under the receiver operating curve (AUC) of 0.939 compared to RB labels with an AUC of 0.858. Training with manual labels performed slightly worse than training with DL labels with an AUC of 0.934. In contrast, training with a public data set resulted in an AUC of 0.720.Our results show that leveraging a rule-based report labeler for weak supervision leads to improved labeling performance. The pneumothorax classification results demonstrate that our proposed deep learning-based labeler can serve as a substitute for manual labeling requiring only 1000 manually annotated reports for training. · The proposed deep learning-based label extraction model for German thoracic radiology reports performs better than the rule-based model.. · Training with limited supervision outperformed training with a small manually labeled data set.. · Using predicted labels for pneumothorax classification from chest radiographs performed equally to using manual annotations.. Wollek A, Haitzer P, Sedlmeyr T et al. Language modelbased labeling of German thoracic radiology reports. Fortschr Röntgenstr 2024; DOI 10.1055/a-2287-5054.

Multi-label local awareness and global co-occurrence priori learning improve chest X-ray classification

Multi-label local awareness and global co-occurrence priori learning improve chest X-ray classification

Decoding Pneumonia: Leveraging CNNS for Accurate Chest X-Ray Classification

Pneumonia is a known potentially fatal lung disease that is frequently referred to as a silent killer since it can lead to lung alveoli filling with pus or fluid, mainly from fungal, viral, or bacterial infections. Chest X-rays are the primary diagnostic tool for pneumonia; however, the diagnosis becomes more complex when other pulmonary disorders such volume loss, haemorrhage, lung cancer, fluid overload, and consequences from radiation or surgery are taken into account. As a result, the interpretation of chest X-rays becomes complex, which makes the development of computer-aided diagnosis systems necessary to help physicians make decisions that are more accurate. In order to diagnose pneumonia from chest X-ray pictures, the research reported here uses a convolutional neural network (CNN) enhanced with a self-attention mechanism. 'Normal' and 'pneumonia' classes are included in the dataset used in the study methodology, and data augmentation techniques are applied to improve the model's resilience. By means of extensive evaluation metrics and visualizations, the study highlights the potential of the suggested model as a useful instrument to aid clinicians in diagnosing pneumonia, consequently reducing the difficulties linked to the interpretation of chest X-rays in the context of various pulmonary conditions.

Combining Graph Transformers Based Multi-Label Active Learning and Informative Data Augmentation for Chest Xray Classification

Informative sample selection in active learning (AL) helps a machine learning system attain optimum performance with minimum labeled samples, thus improving human-in-the-loop computer-aided diagnosis systems with limited labeled data. Data augmentation is highly effective for enlarging datasets with less labeled data. Combining informative sample selection and data augmentation should leverage their respective advantages and improve performance of AL systems. We propose a novel approach to combine informative sample selection and data augmentation for multi-label active learning. Conventional informative sample selection approaches have mostly focused on the single-label case which do not perform optimally in the multi-label setting. We improve upon state-of-the-art multi-label active learning techniques by representing disease labels as graph nodes, use graph attention transformers (GAT) to learn more effective inter-label relationships and identify most informative samples. We generate transformations of these informative samples which are also informative. Experiments on public chest xray datasets show improved results over state-of-the-art multi-label AL techniques in terms of classification performance, learning rates, and robustness. We also perform qualitative analysis to determine the realism of generated images.

Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification

The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.

A Novel Approach to the Technique of Lung Region Segmentation Based on a Deep Learning Model to Diagnose COVID-19 X-ray Images.

The novel coronavirus pandemic has caused a global health crisis, placing immense strain on healthcare systems worldwide. Chest X-ray technology has emerged as a critical tool for the diagnosis and treatment of COVID-19. However, the manual interpretation of chest X-ray films has proven to be inefficient and time-consuming, necessitating the development of an automated classification system. In response to the challenges posed by the COVID-19 pandemic, we aimed to develop a deep learning model that accurately classifies chest X-ray images, specifically focusing on lung regions, to enhance the efficiency and accuracy of COVID-19 and pneumonia diagnosis. We have proposed a novel deep network called "FocusNet" for precise segmentation of lung regions in chest radiographs. This segmentation allows for the accurate extraction of lung contours from chest X-ray images, which are then input into the classification network, ResNet18. By training the model on these segmented lung datasets, we sought to improve the accuracy of classification. The performance of our proposed system was evaluated on three types of lung regions in normal individuals, COVID-19 patients, and those with pneumonia. The average accuracy of the segmentation model (FocusNet) in segmenting lung regions was found to be above 90%. After reclassification of the segmented lung images, the specificities and sensitivities for normal, COVID-19, and pneumonia were excellent, with values of 98.00%, 99.00%, 99.50%, and 98.50%, 100.00%, and 99.00%, respectively. ResNet18 achieved impressive area under the curve (AUC) values of 0.99, 1.00, and 0.99 for classifying normal, COVID-19, and pneumonia, respectively, on the segmented lung datasets. Moreover, the AUC values of the three groups increased by 0.02, 0.02, and 0.06, respectively, when compared to the direct classification of unsegmented original images. Overall, the accuracy of lung region classification after processing the datasets was 99.3%. Our deep learning-based automated chest X-ray classification system, incorporating lung region segmentation using FocusNet and subsequent classification with ResNet18, has significantly improved the accuracy of diagnosing respiratory lung diseases, including COVID-19. The proposed approach has great potential to revolutionize the diagnosis of COVID-19 and other respiratory lung diseases, offering a valuable tool to support healthcare professionals during health crises.

Chest x-ray diagnosis via spatial-channel high-order attention representation learning

Objective. Chest x-ray image representation and learning is an important problem in computer-aided diagnostic area. Existing methods usually adopt CNN or Transformers for feature representation learning and focus on learning effective representations for chest x-ray images. Although good performance can be obtained, however, these works are still limited mainly due to the ignorance of mining the correlations of channels and pay little attention on the local context-aware feature representation of chest x-ray image. Approach. To address these problems, in this paper, we propose a novel spatial-channel high-order attention model (SCHA) for chest x-ray image representation and diagnosis. The proposed network architecture mainly contains three modules, i.e. CEBN, SHAM and CHAM. To be specific, firstly, we introduce a context-enhanced backbone network by employing multi-head self-attention to extract initial features for the input chest x-ray images. Then, we develop a novel SCHA which contains both spatial and channel high-order attention learning branches. For the spatial branch, we develop a novel local biased self-attention mechanism which can capture both local and long-range global dependences of positions to learn rich context-aware representation. For the channel branch, we employ Brownian Distance Covariance to encode the correlation information of channels and regard it as the image representation. Finally, the two learning branches are integrated together for the final multi-label diagnosis classification and prediction. Main results. Experiments on the commonly used datasets including ChestX-ray14 and CheXpert demonstrate that our proposed SCHA approach can obtain better performance when comparing many related approaches. Significance. This study obtains a more discriminative method for chest x-ray classification and provides a technique for computer-aided diagnosis.