Chest X-ray Dataset Research Articles

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.

Class imbalance on medical image classification: towards better evaluation practices for discrimination and calibration performance.

This work aims to assess standard evaluation practices used by the research community for evaluating medical imaging classifiers, with a specific focus on the implications of class imbalance. The analysis is performed on chest X-rays as a case study and encompasses a comprehensive model performance definition, considering both discriminative capabilities and model calibration. We conduct a concise literature review to examine prevailing scientific practices used when evaluating X-ray classifiers. Then, we perform a systematic experiment on two major chest X-ray datasets to showcase a didactic example of the behavior of several performance metrics under different class ratios and highlight how widely adopted metrics can conceal performance in the minority class. Our literature study confirms that: (1) even when dealing with highly imbalanced datasets, the community tends to use metrics that are dominated by the majority class; and (2) it is still uncommon to include calibration studies for chest X-ray classifiers, albeit its importance in the context of healthcare. Moreover, our systematic experiments confirm that current evaluation practices may not reflect model performance in real clinical scenarios and suggest complementary metrics to better reflect the performance of the system in such scenarios. Our analysis underscores the need for enhanced evaluation practices, particularly in the context of class-imbalanced chest X-ray classifiers. We recommend the inclusion of complementary metrics such as the area under the precision-recall curve (AUC-PR), adjusted AUC-PR, and balanced Brier score, to offer a more accurate depiction of system performance in real clinical scenarios, considering metrics that reflect both, discrimination and calibration performance. This study underscores the critical need for refined evaluation metrics in medical imaging classifiers, emphasizing that prevalent metrics may mask poor performance in minority classes, potentially impacting clinical diagnoses and healthcare outcomes. Common scientific practices in papers dealing with X-ray computer-assisted diagnosis (CAD) systems may be misleading. We highlight limitations in reporting of evaluation metrics for X-ray CAD systems in highly imbalanced scenarios. We propose adopting alternative metrics based on experimental evaluation on large-scale datasets.

Robust Stochastic Neural Ensemble Learning With Noisy Labels for Thoracic Disease Classification.

Chest radiography is the most common radiology examination for thoracic disease diagnosis, such as pneumonia. A tremendous number of chest X-rays prompt data-driven deep learning models in constructing computer-aided diagnosis systems for thoracic diseases. However, in realistic radiology practice, a deep learning-based model often suffers from performance degradation when trained on data with noisy labels possibly caused by different types of annotation biases. To this end, we present a novel stochastic neural ensemble learning (SNEL) framework for robust thoracic disease diagnosis using chest X-rays. The core idea of our method is to learn from noisy labels by constructing model ensembles and designing noise-robust loss functions. Specifically, we propose a fast neural ensemble method that collects parameters simultaneously across model instances and along optimization trajectories. Moreover, we propose a loss function that both optimizes a robust measure and characterizes a diversity measure of ensembles. We evaluated our proposed SNEL method on three publicly available hospital-scale chest X-ray datasets. The experimental results indicate that our method outperforms competing methods and demonstrate the effectiveness and robustness of our method in learning from noisy labels. Our code is available at https://github.com/hywang01/SNEL.

Comparative Analysis of VGG16, RESNET50, AND CNN Models for Lung Disease Prediction: A Deep Learning Approach

Abstract: Utilizing deep learning methods in medical image analysis has shown promise in enhancing disease detection and diagnosis. In this study, we conducted a detailed comparative analysis of threewidely recognized convolutional neural network (CNN) architectures: VGG16, ResNet50, and a custom CNN model tailored for pneumonia and COVID-19 detection from chest X-ray images. Leveraging transfer learning techniques and meticulously curated datasets, we evaluated the models' performance in accurately identifying respiratory diseases. Our investigation utilized two publicly available datasets, the ChestX-ray14 dataset and the NIH Chest X-ray Dataset, both annotated for pneumonia and COVID-19. Prior to model training, we conducted thorough preprocessing to ensure optimal data quality and consistency. Through rigorous experimentation, we assessed the models' accuracy, sensitivity, and specificity in disease detection. The results revealed nuanced differences in model performance across disease categories. While VGG16 demonstrated robust accuracy in pneumonia detection, ResNet50 exhibited enhanced sensitivity and specificity in identifying COVID-19 cases. Our custom CNN model, leveraging insights from both architectures, showcased competitive performance, emphasizing the importance of tailored model design for optimal diagnostic outcomes. Through comprehensive analysis and discussion, we elucidated the strengths and limitations of each model, considering factors such as computational efficiency, interpretability, and generalizability. Our findings underscore the potential of deep learningbased diagnostic tools in supporting healthcare professionals in timely and accurate disease diagnosis

Towards COVID-19 detection and classification using optimal efficient Densenet model on chest X-ray images

COVID-19 detection is mainly based on molecular testing, especially polymerase chain reaction (PCR) analyses, to find the occurrence of the virus's genetic material in respiratory samples. Computed tomography (CT) scans and X-rays are the two chest imaging techniques, which may provide more details on lungs, aiding in the diagnosis. Radiological findings in COVID-19 cases typically involve special features such as bilateral involvement, ground-glass opacities, and consolidation of the lungs. Deep learning (DL) methods, particularly convolutional neural networks (CNNs) are used for analyzing COVID-19 infection. By training these techniques on large datasets of annotated chest X-rays (CXRs), researches have attained superior outcomes in precisely detecting the possible COVID-19 cases. Leveraging artificial intelligence together with radiological expertise holds great potential for facilitating prompt intervention in the ongoing global efforts and improving the efficiency of early detection. This article focuses on the design of mother optimization algorithm with DL-enabled detection and classification (MOA-DLEDC) technique for COVID-19 diagnosis using CXR images. In the MOA-DLEDC technique, the adaptive median filtering (AMF) approach is used to eliminate the existence of noise. In addition, the complex and intrinsic feature patterns are derived from the Efficient DenseNet model. Besides, the hyperparameter optimization of the Efficient DenseNet model takes place using MOA. For the COVID-19 detection process, the MOA-DLEDC technique applies sparse autoencoder (SAE) model. A detailed set of experiments was conducted on the CXR dataset to highlight the promising results of the MOA-DLEDC technique. The extensive results inferred that the used technique gains superior performance over other models with maximum accuracy of 99.59 %.

Pneumonia prediction using deep learning in chest X-ray Images

Pneumonia, a potentially fatal lung disease caused by viral or bacterial infection, poses challenges in diagnosis from chest X-ray images due to similarities with other lung infections. This research aims to develop a computer-aided system for pneumonia detection in children, enhancing diagnostic accuracy. In this paper, five established deep learning models such as VGG-16, VGG-19, ResNet-50, Inception-V3, Xception pre-trained on ImageNet have been used. These models have been applied on the chest X-ray dataset to optimize performance. Xception provides recall, specificity, accuracy and AUC of 97.43%, 91.02%, 95.06% and 94.23%, respectively.

Automated detection of COVID-19 and pneumonia diseases using data mining and transfer learning algorithms with focal loss from chest X-ray images

Pneumonia is a lung infection that produces severe inflammation, which can be caused by viruses, bacteria, or fungi. The global occurrence of pneumonia cases has been notably affected by the outbreak of Coronavirus Disease 2019 (COVID-19). Recent studies have demonstrated the capability of Deep Learning (DL) algorithms in detecting COVID-19 and pneumonia in medical images like X-rays. In this study, we employ four different Chest X-ray (CXR) datasets to evaluate four pre-trained Convolutional Neural Network (CNN) models: InceptionResNet V2, Inception V3, Xception, and MobileNet. These models were fine-tuned using either the Cross-Entropy (CE) loss function only for balanced datasets or separately using both the CE and Focal Loss (FL) functions for imbalanced datasets. For the first dataset, the InceptionResNet V2 model achieved the highest multi-classification accuracy of 88.63%. The superior model for the second and fourth datasets is Inception V3, with 94.35% and 97.67% multi-classification accuracy, respectively. For the third dataset, the best model is Xception, with a binary classification accuracy of 100.00%. Our results emphasize the significance of using the FL function in solving class imbalance problems in DL models. Additionally, it highlights the effectiveness of individual DL models in detecting different pneumonia infections using different CXR datasets.

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning.

Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency problem and improve autonomic abnormality localization performance of heterogeneous chest X-ray image analysis, particularly in detecting abnormalities, by developing a self-supervised learning strategy called "BarlwoTwins-CXR". We utilized two publicly available datasets: the NIH Chest X-ray Dataset and the VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training process. Initially, self-supervised pre-training was performed using an adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone pre-trained on ImageNet. This was followed by supervised fine-tuning on the VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN). The study employed mean Average Precision (mAP) at an Intersection over Union (IoU) of 50% and Area Under the Curve (AUC) for performance evaluation. Our experiments showed a significant improvement in model performance with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy compared to traditional ImageNet pre-trained models. In addition, the Ablation CAM method revealed enhanced precision in localizing chest abnormalities. The study involved 112,120 images from the NIH dataset and 18,000 images from the VinDr-CXR dataset, indicating robust training and testing samples. BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios. Our experiment results demonstrate the potential of using self-supervised learning to improve the generalizability of models in medical settings with limited amounts of heterogeneous data. This approach can be instrumental in aiding radiologists, particularly in high-workload environments, offering a promising direction for future AI-driven healthcare solutions.

Prediction of Chest X-Ray Abnormalities using Ensemble Learning

Abstract: Chest X-rays are a cornerstone of medical imaging, enabling rapid and cost-effective diagnosis of numerous chest pathologies. However, limitations in radiologist availability and long wait times can significantly delay diagnoses, potentially impacting patient outcomes. This study explores the potential of deep learning to address these challenges. Deep learning algorithms can analyse medical images and identify patterns associated with various diseases. In this study, Various deep learning models were trained and evaluated, including DenseNet121, EfficientNetB1, Xception, and an ensemble model combining EfficientNetB1 and Xception, using a random sample of 5,606 chest X-ray images from the National Institutes of Health (NIH) chest X-ray dataset. The ensemble model achieved the most promising results, demonstrating an accuracy of 72.66% and an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 76.05%

Improving AI Model Performance by Augmenting Synthetic Data

Improving AI Model Performance by Augmenting Synthetic Data

Attentional decoder networks for chest X-ray image recognition on high-resolution features

Background and objective:This paper introduces an encoder–decoder-based attentional decoder network to recognize small-size lesions in chest X-ray images. In the encoder-only network, small-size lesions disappear during the down-sampling steps or are indistinguishable in the low-resolution feature maps. To address these issues, the proposed network processes images in the encoder–decoder architecture similar to U-Net families and classifies lesions by globally pooling high-resolution feature maps. However, two challenging obstacles prohibit U-Net families from being extended to classification: (1) the up-sampling procedure consumes considerable resources, and (2) there needs to be an effective pooling approach for the high-resolution feature maps. Methods:Therefore, the proposed network employs a lightweight attentional decoder and harmonic magnitude transform. The attentional decoder up-samples the given features with the low-resolution features as the key and value while the high-resolution features as the query. Since multi-scaled features interact, up-sampled features embody global context at a high resolution, maintaining pathological locality. In addition, harmonic magnitude transform is devised for pooling high-resolution feature maps in the frequency domain. We borrow the shift theorem of the Fourier transform to preserve the translation invariant property and further reduce the parameters of the pooling layer by an efficient embedding strategy. Results:The proposed network achieves state-of-the-art classification performance on the three public chest X-ray datasets, such as NIH, CheXpert, and MIMIC-CXR. Conclusions:In conclusion, the proposed efficient encoder–decoder network recognizes small-size lesions well in chest X-ray images by efficiently up-sampling feature maps through an attentional decoder and processing high-resolution feature maps with harmonic magnitude transform. We open-source our implementation at https://github.com/Lab-LVM/ADNet.

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

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

Semantically redundant training data removal and deep model classification performance: A study with chest X-rays

Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Also, the common use of augmentation methods to generate variety in DL training could limit performance when indiscriminately applied to such data. We hypothesize that semantic redundancy would therefore tend to lower performance and limit generalizability to unseen data and question its impact on classifier performance even with large data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data and demonstrate using the publicly available NIH chest X-ray dataset that the model trained on the resulting informative subset of training data significantly outperforms the model trained on the full training set, during both internal (recall: 0.7164 vs 0.6597, p<0.05) and external testing (recall: 0.3185 vs 0.2589, p<0.05). Our findings emphasize the importance of information-oriented training sample selection as opposed to the conventional practice of using all available training data.

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays.The proposed method is tested against other pneumothorax segmentation techniques using a dataset of ‘chest X-rays’ with ‘pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.

A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

Enhancing Pneumonia Diagnosis Through Chest Imaging and Machine Learning A Comprehensive Ensemble Approach with Diverse CNN Architectures

Abstract: This research presents a novel approach for pneumonia diagnosis in chest X-ray images utilizing an ensemble of convolutional neural networks (CNNs). The proposed system integrates state-of-the-art architectures such as ResNet, DenseNet, InceptionV3, MobileNet, and AlexNet, helping transfer learning to fine-tune these models on a curated chest X-ray dataset obtained from Kaggle. The dataset comprises two classes: normal and pneumonia. The ensemble methodology combines the predictive strengths of individual CNN models, harnessing their diverse feature extraction capabilities. A key innovation lies in the incorporation of the AlexNet architecture into the ensemble, aiming to further enhance the ensemble's discriminative power. The system undergoes a comprehensive training, validation, and testing pipeline, culminating in real-time predictions on new chest X-ray images. The experimental results showcase the effectiveness of the ensemble approach, demonstrating improved accuracy and robustness in pneumonia detection compared to individual models. The incorporation of AlexNet contributes unique features to the ensemble, ighlighting the potential of diverse model architectures in enhancing diagnostic performance

Deep learning-based automatic segmentation of COVID-19 in chest X-ray images using ensemble neural net sentinel algorithm

IntroductionCOVID-19 has affected billions of people worldwide and has serious public health effects. Automatic image segmentation is crucial to improve clinical decision-making for full assessments of illness management as well as monitoring. It plays a crucial part in detecting, diagnosing, and tracking diseases by allowing for the exact or accurate segmentation of diseased regions. Automatic segmentation and classifying medical images remains the most difficult obstacle in medical diagnosis, however, because of the lack of annotated medical images. MethodsIn this article, we propose the Ensemble Neural Net Sentinel Algorithm (ENNSA), and unique deep learning (DL) method for the segmentation and classification of COVID-19. Chest X-ray dataset is first collected from Kaggle and preprocessed. The features in the processed image are retrieved using the Insistent Grey Level Co-occurrence Matrix (IGLCM). ResultsThe suggested ENNSA outperforms current techniques in terms of accuracy (99.25), precision (100), recall (43), and F1 score (61) regardless of unbalanced datasets, indicating its promise as a diagnostic tool for respiratory diseases. ConclusionIn addition, the segmentation approach is used to separate the chosen features for COVID-19 classification from the chest X-ray images. The investigational outcomes demonstrate that our proposed strategy outperforms currently used methods in classifying COVID-19.

CP_DeepNet: a novel automated system for COVID-19 and pneumonia detection through lung X-rays

AbstractIn recent years, the COVID-19 outbreak has affected humanity across the globe. The frequent symptoms of COVID-19 are identical to the normal flu, such as fever and cough. COVID-19 disseminates rapidly, and it has become a prominent cause of mortality. Nowadays, the new wave of COVID-19 has created significant impacts in China. This virus can have detrimental effects on people of all ages, particularly the elderly, due to their weak immune systems. The real-time polymerase chain reaction (RT-PCR) examination is typically performed for the identification of coronavirus. RT-PCR is an expensive and time requiring method, accompanied by a significant rate of false negative detections. Therefore, it is mandatory to develop an inexpensive, fast, and reliable method to detect COVID-19. X-ray images are generally utilized to detect diverse respiratory conditions like pulmonary infections, breathlessness syndrome, lung cancer, air collection in spaces of the lungs, etc. This study has also utilized a chest X-ray dataset to identify COVID-19 and pneumonia. In this research work, we proposed a novel deep learning model CP_DeepNet, which is based on a pre-trained deep learning model such as SqueezeNet, and further added three blocks of convolutional layers to it for assessing the classification efficacy. Furthermore, we employed a data augmentation method for generating more images to overcome the problem of model overfitting. We utilized COVID-19 radiograph dataset for evaluating the performance of the proposed model. To elaborate further, we obtained significant results with accuracy of 99.32%, a precision of 100%, a recall of 99%, a specificity of 99.2%, an area under the curve of 99.78%, and an F1-score of 99.49% on CP_DeepNet for the binary classification of COVID-19 and normal class. We also employed CP_DeepNet for the multiclass classification of COVID-19, pneumonia, and normal person, in which CP_DeepNet achieved accuracy, precision, recall, specificity, area under curve, and F1-score of 99.62%, 99.79%, 99.52%, 99.69, 99.62, and 99.72%, respectively. Comparative analysis of experimental results with different preexisting techniques shows that the proposed model is more dependable as compared to RT-PCR and other prevailing modern techniques for the detection of COVID-19.

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.

Enhancing pediatric pneumonia diagnosis through masked autoencoders

Pneumonia, an inflammatory lung condition primarily triggered by bacteria, viruses, or fungi, presents distinctive challenges in pediatric cases due to the unique characteristics of the respiratory system and the potential for rapid deterioration. Timely diagnosis is crucial, particularly in children under 5, who have immature immune systems, making them more susceptible to pneumonia. While chest X-rays are indispensable for diagnosis, challenges arise from subtle radiographic findings, varied clinical presentations, and the subjectivity of interpretations, especially in pediatric cases. Deep learning, particularly transfer learning, has shown promise in improving pneumonia diagnosis by leveraging large labeled datasets. However, the scarcity of labeled data for pediatric chest X-rays presents a hurdle in effective model training. To address this challenge, we explore the potential of self-supervised learning, focusing on the Masked Autoencoder (MAE). By pretraining the MAE model on adult chest X-ray images and fine-tuning the pretrained model on a pediatric pneumonia chest X-ray dataset, we aim to overcome data scarcity issues and enhance diagnostic accuracy for pediatric pneumonia. The proposed approach demonstrated competitive performance an AUC of 0.996 and an accuracy of 95.89% in distinguishing between normal and pneumonia. Additionally, the approach exhibited high AUC values (normal: 0.997, bacterial pneumonia: 0.983, viral pneumonia: 0.956) and an accuracy of 93.86% in classifying normal, bacterial pneumonia, and viral pneumonia. This study also investigated the impact of different masking ratios during pretraining and explored the labeled data efficiency of the MAE model, presenting enhanced diagnostic capabilities for pediatric pneumonia.