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Introduction: Existing deep learning models for lung opacity detection primarily focus on grayscale images, overlooking the potential benefits of colour map transformations. In this study, we address this gap by fine-tuning Dark-Net-53 and ResNet-101 deep learning models using both grayscale and 16 distinct colour maps to enhance disease classification. Through transfer learning and data augmentation, we explore how colour map transformations can improve the models' diagnostic accuracy, sensitivity, and specificity, aiming to optimize deep learning performance for better clinical decision-making. Methods: A total of 11,342 chest X-ray images, consisting of normal and disease-related lung opacity images, were used in this study. These images were pre-processed into 16 different colourmaps to enhance visualization. The DarkNet-53 and ResNet-101 deep learning models were fine-tuned using transfer learning and standard data augmentation techniques. Performance metrics, including accuracy, sensitivity, and specificity, were calculated to evaluate the models. Results: The DarkNet-53 model achieved an average accuracy of 89.9%, sensitivity of 88.1%, and specificity of 92.9% across various colourmaps. The ResNet-101 model demonstrated similar performance with an average accuracy of 89.9%, sensitivity of 88.2%, and specificity of 92.8%. The "Spring" colourmap resulted in the highest accuracy and sensitivity for the DarkNet-53 model, while the "Copper" colourmap was optimal for the ResNet-101 model. Conclusion: The findings highlight the importance of optimizing colourmap selection to enhance the performance of deep learning models for disease-related lung opacity detection. The careful choice of colourmaps can significantly improve model accuracy, sensitivity, and specificity, leading to better diagnostic precision and patient outcomes in managing respiratory diseases. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis (TB) is a bacterial disease that mainly affects the lungs, but it can also impact other parts of the body, such as the brain, bones, and kidneys. The disease is caused by a bacterium called Mycobacterium tuberculosis and spreads through the air when an infected person coughs or sneezes. TB can be inactive or active; in its active state, noticeable symptoms appear, and it can be transmitted to others. There are ongoing challenges in fighting TB, including resistance to medications, co-infections, and limited resources in areas heavily affected by the disease. These issues make it challenging to eradicate TB. Objective: Timely and precise diagnosis is essential for effective control, especially since TB often goes undetected and untreated, particularly in remote and under-resourced locations. Chest X-ray (CXR) images are commonly used to diagnose TB. However, difficulties can arise due to unusual findings on X-rays and a shortage of radiologists in high-infection areas. Method: To address these challenges, a computer-aided diagnosis (CAD) system that uses the vision transformer (ViT) technique has been developed to accurately identify TB in CXR images. This innovative hybrid CAD approach combines ViT with Principal Component Analysis (PCA) and machine learning (ML) techniques for TB classification, introducing a new method in this field. In the hybrid CAD system, ViT is used for deep feature extraction as a base model, PCA is used to reduce feature dimensions, and various ML methods are used to classify TB. This system allows for quickly identifying TB, enabling timely medical action and improving patient outcomes. Additionally, it streamlines the diagnostic process, reducing time and costs for patients and lessening the workload on healthcare professionals. The TB chest X-ray dataset was utilized to train and evaluate the proposed CAD system, which underwent pre-processing techniques like resizing, scaling, and noise removal to improve diagnostic accuracy. Results: The performance of our CAD model was assessed against existing models, yielding excellent results. The model achieved remarkable metrics: an average precision of 99.90%, recall of 99.52%, F1-score of 99.71%, accuracy of 99.84%, false negative rate (FNR) of 0.48%, specificity of 99.52%, and negative predictive value (NPV) of 99.90%. Conclusions: This evaluation highlights the superior performance of our model compared to the latest available classifiers. [ABSTRACT FROM AUTHOR]

Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based object detection methods for automatically identifying and annotating abnormal regions in CXR images. Methods: We developed and tested our models using disease-labeled CXR images and location-bounding boxes from E-Da Hospital. Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. To address the issue of limited examples for certain diseases, we also investigated few-shot object detection techniques. We compared convolutional neural networks (CNNs) and Transformer-based models to determine the most effective architecture for medical image analysis. Results: The findings show that background image proportions greatly influenced model inference. Moreover, schemes incorporating binary classification consistently improved performance, and CNN-based models outperformed Transformer-based models across all scenarios. Conclusions: We have developed a more efficient and reliable system for the automated detection of disease labels and location bounding boxes in CXR images. [ABSTRACT FROM AUTHOR]

Chest radiography is an indispensable diagnostic method for detecting a variety of medical conditions, such as infections, tumors, injuries, etc. Millions of chest X-ray examinations are conducted annually, providing crucial information about the functioning of the respiratory and circulatory systems. The conventional approach to quantifying cardiothoracic indices, such as the Lupi and Moore indices and the Cardiothoracic Index (CTI), requires considerable time and effort from radiologists. Consequently, it calls for the exploration of computational methods for improvement through deep learning. In this study, we addressed the challenge of automating the calculation of these cardiometric indices. We engaged four experienced radiologists to manually label 800 chest X-ray images each. Using these labeled images, we trained a deep learning model that achieved the level of performance comparable to that of a professional radiologist. Additionally, we have replaced the central points of the indices with landmarks based on the vertebrae, improving the accuracy. The use of AI led to improved accuracy of correct predictions, increasing it from 85.94% to 87.34% for the MOORE coefficient and from 87.55% to 90.67% for the LUPI coefficient.

Background The government of Korea implemented a strategy of prevention and early diagnosis in high-risk groups to reduce the tuberculosis (TB) burden. This study aims to investigate the TB epidemiology and gap in understanding of TB prevalence among homeless individuals by analyzing active TB chest X-ray (CXR) screening results in Korea. Methods The Korean National Tuberculosis Association conducted active TB screening with CXR for homeless groups from January 1 to December 31, 2021. Sputum acid-fast bacilli smear and culture were performed for the subjects suggestive of TB on CXR. We performed a cross-sectional analysis of the data in comparison with the national health screening results from the general population. Results Among 17,713 homeless persons, 40 (0.23%), 3,077 (17.37%), and 79 (0.45%) were categorized as suggested TB, inactive TB, and observation required, respectively. Prevalence of suggested TB in the homeless was significantly higher (3–5 fold) than in Univerthe national general health screening based on age category (p

Histiocytic sarcoma is an extremely rare disease that's hard to diagnose and treat, often leading to a poor prognosis. Here, we present a case report detailing a rare occurrence of HS in a 37-year-old pregnant woman who first presented with left shoulder pain, palpitations, and a productive cough at 20 weeks of gestation. Her diagnostic evaluations were performed, including different imaging modalities such as chest X-rays, CT scans, and MRI. Imaging revealed a large mediastinal mass with extensive involvement of the adrenal glands, lungs, and lymph nodes. The definitive diagnosis of HS is based on pathological and morphological features, and the immunohistochemistry report plays a key role. In our case, the diagnosis of HS was confirmed through pathological evaluation and immunohistochemistry, with a positive CD68 result obtained from a supraclavicular lymph node biopsy. A hospital committee comprising medical specialists like hematologists-oncologists, pathologists, pulmonologists, and obstetricians was brought together to assess the case collectively. The patient received chemotherapy, which alleviated her symptoms and maintained her condition. Based on the committee's recommendations, despite a healthy fetus and normal obstetric sonograms, the decision was made to terminate the pregnancy with the consent of the patient and her family. Despite initial improvement postchemotherapy, the patient's condition worsened, necessitating intubation. Tragically, two months after the initial admission, the patient passed away due to severe complications. In this case report, we provide a literature review and review of the patient's imaging reports. Since the patient is pregnant and HS is uncommon, it's important to highlight that this case is unique and worth sharing.

Coronavirus Disease 2019 (COVID-19) is extremely contagious with a very high mortality rate. Effective and early diagnosis of COVID-19 is therefore crucial when treating patients and limiting its spread. The currently available methods for reliably identifying COVID are time-consuming. Infected people display various symptoms, some of which can be manifested by radiographic imaging such as chest X-rays and CT scans. Recently, many advanced machine learning and deep learning models have been proposed for predicting COVID using chest X-rays and CT scans that have paved the way for computer-aided COVID-19 diagnosis (CACD) systems. Unfortunately, most of these studies employ specific model(s) using a specific dataset making comparison difficult and inconclusive. We still lack a clear picture on which technique is best for a reliable CACD system. In this study, we provide a comprehensive analysis to determine if a CACD system can be developed that can reliably and automatically predict COVID-19 with zero human intervention using currently available tools and techniques? For this purpose, we explore and implement five machine learning models (SVM, LR, RF, KNN and ANN) and three pre-trained deep learning classifiers (VGG-16, Xception and ResNet-50) to compare their performance using 17 benchmark chest X-rays and CT-scans datasets to predict normal and infected samples. Using different classifiers and different datasets, we show that VGG16 with a superior average accuracy (99.10%) is the most suited classifier for CACD when chest X-rays are used. For CT scans, RF can also be used in addition to VGG16 as both records an average accuracy of 93% overall CT scan datasets. Based on the number of experiments, and an average accuracy of 99.10% for the chest X-rays datasets, we conclude that a reliable CACD system is possible. [ABSTRACT FROM AUTHOR]

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., [ABSTRACT FROM AUTHOR]

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems. [ABSTRACT FROM AUTHOR]

Pneumonia is a serious inflammatory disease that causes lung ulcers, and it is one of the leading reasons for pediatric death in the world. Chest X-rays are perhaps the most commonly utilized modalities to recognize pneumonia. Generally, the illness could be analyzed by a specialist radiologist. But for some reason, the diagnosis may be subjective. Thus, the physicians must be guided by computer-aided diagnosis frameworks in this challenging task. In this study, we propose a combined deep learning architecture to identify pneumonia in chest radiography images. We first, use Adaptive Median Filter for images enhancement, then we employ a regularized Convolutional Neural Network for features extraction, and then we use Long Short Term Memory as a classifier. Finally, the attention mechanism is used to direct the network attention to relevant features. The suggested approach was tested on two publicly available pneumonia X-ray datasets provided by Kermany and the Radiological Society of North America. On the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 99.91% and 88.86%, respectively. In the last stage of our experiments, we employed a Grad-CAM-based color visualization technique to precisely interpret the detection of pneumonia in radiological images. The results outperformed those of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and validate a deep learning survival prediction model using chest X-ray (DLSPCXR) in patients with HF. The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest X-ray (CXR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n = 247) and validation (n = 106) datasets. Univariate and multivariate Cox analysis were conducted on the training dataset to develop clinical and imaging survival prediction models. The DLSPCXR was trained and the selected clinical parameters were incorporated into DLSPCXR to establish a new model called DLSPinteg. Discrimination performance was evaluated using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-years survival. Delong's test was employed for the comparison of differences between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by the Kaplan–Meier curve. In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (sPAP) > 50 mmHg, New York Heart Association (NYHA) functional class III–IV and cardiothoracic ratio (CTR) ≥ 0.62 in CXR were independent predictors of poor prognosis in patients with HF. Based on the receiver operating characteristic (ROC) curve analysis, DLSPCXR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs. 0.561, P = 0.01). DLSPinteg as the optimal model outperforms the clinical Cox model (AUC: 0.826 vs. 0.633, P = 0.03), imaging Cox model (AUC: 0.826 vs. 0.555, P < 0.001), and DLSPCXR (AUC: 0.826 vs. 0.767, P = 0.06). Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Tuberculosis caused by the infection of Mycobacterium. It is the fifth major source of death and one of the greatest threats to humans in the modern world. Thus, it needs to be detected at an earlier stage using the chest X-rays (CXR) image for precise identification and treatment. The suggested scheme's main goal is to identify this deadly disease using CXR with improved classification accuracy. This detection process comprises pre-processing, noise removal, balancing of image level, application of the Double Attention Res-U-Net-based Deep Neural Network (DARUNDNN) model, and optimization of deep learning features using the Dingo Optimization Algorithm for achieving better accuracy. The experimental validation of the proposed DARUNDNN model is conducted using benchmark datasets, namely Montgomery, Shenzhen, and National Institutes of Health CXR images. The results obtained using the Shenzhen dataset confirm that the proposed DARUNDNN model is efficient in achieving better accuracy of 98.92%, specificity of 97.24%, and sensitivity of 98.86% with a least error of 1.6 compared to the benchmarked models used for investigation. Moreover, the experimental validation conducted using the Montgomery County dataset also confirmed an excellent accuracy of 98.982%, a specificity of 97.56%, and a sensitivity of 98.52%, with a least error of 1.32 compared to the baseline approaches used for investigation. [ABSTRACT FROM AUTHOR]

Pneumonia is an acute respiratory illness caused by viruses or bacteria. Early detection of pneumonia is important to ensure curative treatment and improve survival rates. Pneumonia detection on chest X-rays (CXR) is important for early diagnosis, effective treatment, monitoring patient progress, and managing public health concerns. It plays a vital role in ensuring that individuals with pneumonia receive the appropriate care they need while contributing to research and disease surveillance efforts. However, the examination of CXRs is a difficult process and is prone to subjective variabilities. The use of artificial intelligence (AI) and deep learning (DL) models can perform the detection and classification of pneumonia on CXR images. With this motivation, this study introduces a new Chaotic Sea Horse Optimization with Deep Learning Method for Pneumonia Detection and Classification (CSHODL-PDC) technique on CXR images. The main intention of the CSHODL-PDC algorithm lies in the automated detection and classification of pneumonia on CXR images. The CSHODL-PDC method initially designs a Gaussian filtering (GF) based noise eradication approach to eliminate the noise. In addition, the CSHODL-PDC technique employs the NASNetLarge model to produce a set of feature vectors. Moreover, an improved fuzzy deep neural network (FDNN) model is applied for the automated identification and classification of pneumonia. Finally, the CSHO algorithm selects the optimal hyperparameter values of the improved FDNN model, demonstrating the novelty of the work. A series of simulation analyses were performed on the CXR Pneumonia dataset from the Kaggle repository. The experimental values inferred the improved performance of the CSHODL-PDC method over recent models with a maximum accuracy of 99.22%, precision of 98.96%, and recall of 99.22%. Therefore, the proposed model can be employed for accurate and automated pneumonia detection. [ABSTRACT FROM AUTHOR]

Currently, the most widespread infectious illness in the world is the coronavirus (COVID-19). The original diagnosis of this illness presents the most obstacle in preventing subsequent infections and their transmission from one person to another. Therefore, it is crucial to employ both a clinical process and an automated diagnostic technology for the quick detection of COVID-19 to stop its spread. Chest X-ray (CXR) images from chest radiography could be used in artificial intelligence (AI) approaches to diagnose COVID-19 with excellent diagnostic accuracy. In this research, a new support vector machine kernel (SVM Kernel) and convolutional neural network (CNN) combination is suggested to classify COVID-19 using X-ray images. The fact that there are relatively few studies in the literature that provide novel solutions, particularly for regression issues, the goal of this study is to look into the creation of new SVM kernels. To categorize CXR pictures into the three categories of COVID-19, pneumonia, and normal utilizing pre-trained CNN models such as AlexNet, ResNet50, ResNet101, VGG-16, and VGG-19, this study proposes a revolutionary SVM Kernel. The results of the suggested approach show that the updated SVM Kernel may be used as a more effective forecasting tool. ResNet50 offers the greatest accuracy 96.2% and produces the best optimization results in a very short amount of time. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CD) is one of the leading causes of death and disability across the globe. Chest x‐rays (CXR) are crucial in detecting chest and CD. The CXR images present helpful information to the radiologist to identify a disease at an earlier stage. Several convolutional neural network (CNN) models for classifying the CXR images have been established. However, there is a demand for significant improvement in CNN models to generalize them in diverse datasets. In addition, healthcare centers require an effective model for identifying CD with limited resources. Therefore, the authors developed a CNN‐based CD detector using CXR images. The proposed research employs the You Only Look Once, version 7 technique to extract features and DenseNet‐161 for classifying the CXR images into normal and abnormal classes. The authors utilized datasets, including CheXpert and VinDr‐CXR, for the performance evaluation. The findings reveal that the proposed study achieves an accuracy and F1‐measure of 97.9, 97.47, 96.85, and 97.77 for the CheXpert and VinDr‐CXR datasets, respectively. The recommended model required fewer parameters of 5.2 M and less computation time for predicting CD. The study's outcome can assist clinicians in detecting CD at the earliest stage. [ABSTRACT FROM AUTHOR]

This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data augmentation technique. The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer. The proposed multiclass system achieved an accuracy (ACC) of 98.3%, recall of 98.3%, precision of 98.7%, and F1-score of 98.7%. Moreover, the robustness of the model was revealed by the Receiver Operating Characteristic (ROC) analysis, which demonstrated an Area Under the Curve (AUC) of 1.00 for edema and normal cases and 0.99 for effusion. The experimental results demonstrate the superiority of the proposed multi-class system, which has the potential to assist clinicians in timely and accurate diagnosis, leading to improved patient outcomes. Notably, ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images, which will aid clinicians in interpreting and localizing abnormalities more effectively. [ABSTRACT FROM AUTHOR]

Among radiological imaging data, Chest X-rays (CXRs) are of great use in observing COVID-19 manifestations. For mass screening, using CXRs, a computationally efficient AI-driven tool is the must to detect COVID-19-positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19-positive cases using CXRs, with no false negatives. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models. The shallow CNN-tailored architecture was validated using 321 COVID-19-positive CXRs. In addition to COVID-19-positive cases, another set of non-COVID-19 5856 cases (publicly available, source: Kaggle) was taken into account, consisting of normal, viral, and bacterial pneumonia cases. In our experimental tests, to avoid possible bias, 5-fold cross-validation was followed, and both balanced and imbalanced datasets were used. The proposed model achieved the highest possible accuracy of 99.69%, sensitivity of 1.0, where AUC was 0.9995. Furthermore, the reported false positive rate was only 0.0015 for 5856 COVID-19-negative cases. Our results stated that the proposed CNN could possibly be used for mass screening. Using the exact same set of CXR collection, the current results were better than other deep learning models and major state-of-the-art works. [ABSTRACT FROM AUTHOR]

SARS (Severe Acute Respiratory Syndrome) or COVID 19, When a sick person talks, sneezes, or coughs, microscopic droplets of mucus or saliva are discharged from their respiratory system, carrying the corona virus-2. It spreads quickly by direct contact with an infected person, touching, or holding contaminated things or surfaces. Pneumonia is a different viral illness that is often caused by an infection brought on by a bacterium in the lung alveoli. Pus accumulates in lungs' tissue that has been infected and is inflamed. Experts perform physical examines and evaluate the patient using chest X-rays, ultrasounds, or lung biopsies to ascertain whether the patient has these conditions. The patient will die as a result of a misdiagnosis, inadequate treatment, and illness neglect. Deep learning advancements assist medical professionals in diagnosing individuals with various disorders by supporting their procedure for making decisions. Using chest X-ray image, researcher uses a versatile or effective deep learning method which applies the CNN model to anticipate and identify patients who are both non-impacted and impacted from the condition. An accuracy rate was achieved by the trained model during the performance training. According to test results, the researcher’s study identify and forecast COVID 19 and Non-COVID. [ABSTRACT FROM AUTHOR]

Background and Aims Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. Methods A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. Results The model yielded an AUROC of 0.79 (0.76–0.81) for SLVH, 0.80 (0.77–0.84) for DLV, and 0.80 (0.78–0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. Conclusions Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available. [ABSTRACT FROM AUTHOR]

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Pneumonia, a lung infection caused by viral or bacterial agents, poses a significant health risk by affecting one or both lungs in humans. Accurate diagnosis, particularly in pediatric cases, is crucial for timely intervention. chest X-rays (CXRs) are a common and non-invasive diagnostic tool to detect pneumonia-related abnormalities. Nonetheless, the minimal radiation exposure suitable for pediatric diagnosis poses a challenge in accurately detecting pneumonia in children. This work proposes a concatenation model that combines two pre-trained convolutional neural networks (CNNs) depending on the transfer learning (TL) technique and optimizes the training parameters to build a highly accurate model for detecting pediatric pneumonia from CXR images. The concatenated extracted features from the two pre-trained CNNs are passed through a convolutional layer to select more valuable semantic features to reduce the extracted features, which helps reduce the model parameters and execution time. Experimental results demonstrate that the feature concatenation technique, along with optimization of training parameters, surpasses the performance of individual CNNs and several state-of-the-art methods. The proposed method achieves a classification accuracy of 98.5%, precision of 99.5%, sensitivity of 98.4%, and F1 score of 99.1%. The primary objective of the proposed approach is to aid radiologists in achieving accurate pneumonia diagnosis in real-time. [ABSTRACT FROM AUTHOR]

BackgroundSuppression of thoracic bone shadows on chest X-rays (CXRs) can improve the diagnosis of pulmonary disease. Previous approaches can be categorized as either unsupervised physical models or supervised deep learning models. Physical models can remove the entire ribcage and preserve the morphological lung details but are impractical due to the extremely long processing time. Machine learning (ML) methods are computationally efficient but are limited by the available ground truth (GT) for effective and robust training, resulting in suboptimal results.PurposeTo improve bone shadow suppression, we propose a generalizable yet efficient workflow for CXR rib suppression by combining physical and ML methods.Materials and methodOur pipeline consists of two stages: (1) pair generation with GT bone shadows eliminated by a physical model in spatially transformed gradient fields; and (2) a fully supervised image denoising network trained on stage-one datasets for fast rib removal from incoming CXRs. For stage two, we designed a densely connected network called SADXNet, combined with a peak signal-to-noise ratio and a multi-scale structure similarity index measure as the loss function to suppress the bony structures. SADXNet organizes the spatial filters in a U shape and preserves the feature map dimension throughout the network flow.ResultsVisually, SADXNet can suppress the rib edges near the lung wall/vertebra without compromising the vessel/abnormality conspicuity. Quantitively, it achieves an RMSE of ~0 compared with the physical model generated GTs, during testing with one prediction in

Pulmonary diseases impact lung functionality and can cause health complications. X-ray imaging is an initial diagnostic approach for evaluating lung conditions. Manual segmentation of lung infections from X-rays is time-consuming and subjective. Automated segmentation has gained interest to reduce clinician workload. Semantic segmentation involves labelling individual pixels in X-rays to highlight infected regions. This article presents PulmonU-Net, an innovative semantic segmentation model using PulmonNet modules as the base network to highlight infected areas in chest X-rays. PulmonNet modules leverage global and local chest X-ray characteristics to create intricate feature maps. Incorporating leaky ReLU activation enables uninterrupted neuron functioning during learning. By adding PulmonNet modules in the encoder's deeper layers, the model addresses vanishing gradients and improves dice similarity coefficient to 94.25%. Real-time testing and prediction visualization demonstrate PulmonU-Net's effectiveness for automated lung infection segmentation from chest X-rays.

Tuberculosis (TB) stands as the leading cause of death and a significant threat to humanity in the contemporary world. Early detection of TB is crucial for precise identification and treatment, and Chest X-Rays (CXR) serve as a valuable tool in this regard. Computer-Aided Diagnosis (CAD) systems play a vital role in easing the classification process of active and latent TB. This paper uses an approach called the Double Attention Res-U-Net-based Deep Neural Network (DARUNDNN) to enhance TB detection in the lungs. The detection process involves pre-processing, noise removal, image level balancing, the application of the DARUNDNN model and using the Whale Optimization Algorithm (WOA) for improved accuracy. Experimental validation using Montgomery Country (MC), Shenzhen China (SC), and NIH CXR Datasets compares the results with U-Net, AlexNet, GoogleNet, and convolutional neural network (CNN) models. The findings, particularly from the SC dataset, demonstrate the efficiency of the proposed DARUNDNN model with an accuracy of 98.6%, specificity of 96.24%, and sensitivity of 97.66%, outperforming benchmarked deep learning models. Additionally, validation with the MC dataset reveals an excellent accuracy of 98%, specificity of 97.56%, and sensitivity of 98.52%. [ABSTRACT FROM AUTHOR]

Deep learning is revolutionizing radiology report generation (RRG) with the adoption of vision encoder–decoder (VED) frameworks, which transform radiographs into detailed medical reports. Traditional methods, however, often generate reports of limited diversity and struggle with generalization. Our research introduces reinforcement learning and text augmentation to tackle these issues, significantly improving report quality and variability. By employing RadGraph as a reward metric and innovating in text augmentation, we surpass existing benchmarks like BLEU4, ROUGE-L, F1CheXbert, and RadGraph, setting new standards for report accuracy and diversity on MIMIC-CXR and Open-i datasets. Our VED model achieves F1-scores of 66.2 for CheXbert and 37.8 for RadGraph on the MIMIC-CXR dataset, and 54.7 and 45.6 , respectively, on Open-i. These outcomes represent a significant breakthrough in the RRG field. The findings and implementation of the proposed approach, aimed at enhancing diagnostic precision and radiological interpretations in clinical settings, are publicly available on GitHub to encourage further advancements in the field. [ABSTRACT FROM AUTHOR]

Pulmonary diseases impact lung functionality and can cause health complications. X-ray imaging is an initial diagnostic approach for evaluating lung conditions. Manual segmentation of lung infections from X-rays is time-consuming and subjective. Automated segmentation has gained interest to reduce clinician workload. Semantic segmentation involves labelling individual pixels in X-rays to highlight infected regions. This article presents PulmonU-Net, an innovative semantic segmentation model using PulmonNet modules as the base network to highlight infected areas in chest X-rays. PulmonNet modules leverage global and local chest X-ray characteristics to create intricate feature maps. Incorporating leaky ReLU activation enables uninterrupted neuron functioning during learning. By adding PulmonNet modules in the encoder's deeper layers, the model addresses vanishing gradients and improves dice similarity coefficient to 94.25%. Real-time testing and prediction visualization demonstrate PulmonU-Net's effectiveness for automated lung infection segmentation from chest X-rays. [ABSTRACT FROM AUTHOR]

In this paper we propose the classification of radiological patterns with the presence of tuberculosis in X-ray images, it was observed that two to six patterns (consolidation, fibrosis, opacity, opacity, pleural, nodules and cavitations) are present in the radiographs of the patients. It is important to mention that species specialists consider the type of TB pattern in order to provide appropriate treatment. It should be noted that not all medical centres have specialists who can immediately interpret radiological patterns. Considering the above, the aim is to classify patterns by means of a convolutional neural network to help make a more accurate diagnosis on X-rays, so that doctors can recommend immediate treatment and thus avoid infecting more people. For the classification of tuberculosis patterns, a proprietary convolutional neural network (CNN) was proposed and compared against the VGG16, InceptionV3 and ResNet-50 architectures, which were selected based on the results of other radiograph classification research [1]–[3] . The results obtained for the Macro-averange AUC-SVM metric for the proposed architecture and InceptionV3 were 0.80, and for VGG16 it was 0.75, and for the ResNet-50 network it was 0.79. The proposed architecture has better classification results, as does InceptionV3.

Misinterpreting radiographic findings can lead to unnecessary interventions and potential patient harm. The urgency required when responding to the compromised health of trauma patients can increase the likelihood of misinterpreting chest x-rays in critical situations. We present the case report of a trauma patient whose skin fold artifacts were mistaken for pneumothorax on a follow-up chest x-ray, resulting in unnecessary chest tube insertion. We hope to help others differentiate between skin folds and pneumothorax on the chest x-rays of trauma patients by considering factors such as location, shape, sharpness, and vascular markings.

Pneumonia is a potentially life-threatening infectious disease that is typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds or lung biopsies. Accurate diagnosis is crucial as wrong diagnosis, inadequate treatment or lack of treatment can cause serious consequences for patients and may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts in diagnosing pneumonia by assisting in their decision-making process. By leveraging deep learning models, healthcare professionals can enhance diagnostic accuracy and make informed treatment decisions for patients suspected of having pneumonia. In this study, six deep learning models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L are implemented and evaluated. The study also incorporates the Adam optimizer, which effectively adjusts the epoch for all the models. The models are trained on a dataset of 5856 chest X-ray images and show 87.78%, 88.94%, 90.7%, 91.66%, 87.98% and 94.02% accuracy for CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates the highest accuracy and proves its robustness for pneumonia detection. These findings highlight the potential of deep learning models in accurately detecting and predicting pneumonia based on chest X-ray images, providing valuable support in clinical decision-making and improving patient treatment.

IntroductionThis study is a retrospective evaluation of the performance of deep learning models that were developed for the detection of COVID-19 from chest x-rays, undertaken with the goal of assessing the suitability of such systems as clinical decision support tools.MethodsModels were trained on the National COVID-19 Chest Imaging Database (NCCID), a UK-wide multi-centre dataset from 26 different NHS hospitals and evaluated on independent multi-national clinical datasets. The evaluation considers clinical and technical contributors to model error and potential model bias. Model predictions are examined for spurious feature correlations using techniques for explainable prediction.ResultsModels performed adequately on NHS populations, with performance comparable to radiologists, but generalised poorly to international populations. Models performed better in males than females, and performance varied across age groups. Alarmingly, models routinely failed when applied to complex clinical cases with confounding pathologies and when applied to radiologist defined “mild” cases.DiscussionThis comprehensive benchmarking study examines the pitfalls in current practices that have led to impractical model development. Key findings highlight the need for clinician involvement at all stages of model development, from data curation and label definition, to model evaluation, to ensure that all clinical factors and disease features are appropriately considered during model design. This is imperative to ensure automated approaches developed for disease detection are fit-for-purpose in a clinical setting.

Despite a worldwide research involvement in the global COVID-19 pandemic, the research community is still struggling to develop reliable and faster prediction mechanisms for this infectious disease which is distinct from other respiratory diseases. The commonly used clinical RT-PCR test is not widely available in areas with limited testing facilities, and it performs and responds slowly. Using digital chest X-Ray images and CT scan images, recently a number of works are proposed using deep transfer learning and ensemble of these deep models as base classifiers. Though ensemble approaches exhibit better accuracy, they are computational intensive and have slower prediction time. Therefore, to handle computational-intensiveness and to accelerate prediction time without compromising accuracy, a Parallel Ensemble Transfer Learning based Framework for COVID (PETLFC) is proposed for the underlying multi-class classification problem. Three pre-trained convolutional neural network models (VGG16, ResNet18, and DenseNet121) were fine tuned to act as base models for the proposed parallelized bagging-based ensemble to predict COVID-19. The data parallel model is implemented on PARAM SHAVAK HPC system using MPI programming and a dataset of 21,165 chest X-Ray images (10,192 normal, 1345 pneumonia, 3616 COVID-19, and 6012 lung opacity). The results are compared with some state-of-the-art sequential ensemble approaches where the proposed PETLFC was observed to exhibit superior performance. Highlights: • This work proposes a parallel ensemble transfer learning based deep CNN framework for COVID-19 prediction from chest X-Rays. • The deep CNN models which are tuned using transfer learning are: DenseNet-121, VGG-16 and ResNet18. They act as base classifiers for the proposed parallelized bagging-based ensemble. • The underlying problem is formulated using data parallel model and as a multi-class classification problem to differentiate COVID-19 from other similar pulmonary diseases. This enables the model to predict COVID-19 in lesser time due to parallelism, with equal accuracy with state-of-the-art sequential bagging-based ensemble methods. • An in-depth evaluation of the system is carried out considering standard performance metrics like accuracy, precision, recall and F1-score. The parallel system is tested using standard parallel performance metrics like speedup and efficiency. [ABSTRACT FROM AUTHOR]

Background: Peri‐procedural complications associated with cardiac implantable electronic devices are not uncommon. European Society of Cardiology guidelines recommend device checks of all devices within 72 h of implant. European Heart Rhythm Association expert practical guide on Cardiac implantable electronic devices (CIEDs) recommend that a chest x‐ray (CXR) should be performed within 24 h to rule out pneumothorax and document lead positions. First, the rate of peri‐procedural complications associated with CIED implants at our center, as well as patient and/or procedural‐related factors that are associated with higher rates of complications, is analyzed. Second, the yield of the guideline‐recommended measures in the early detection of peri‐procedural complications is examined. Materials and methods: Consecutive de novo transvenous device implants at our center in 2019 were retrospectively analyzed. Patients' demographics, types and indications for device therapy, procedural reports, device checks, and CXRs were obtained from the hospital electronic records. Results: A total of 578 patients (Age 74 ± 16 years, 68% male) were included. All patients had routine post‐procedure CXRs and device checks. There were 16 (2.8%) complications; 7 (1.2%) pneumothoraxes, 6 (1%) pericardial effusions, and 3 (0.5%) lead displacements. Procedure time correlated significantly with complications; in uncomplicated cases it was 99 ± 43 min versus 127 ± 50 min in procedures associated with complications (p =.02). Conclusions: Routine post CIED implantation CXRs can detect early peri‐procedural complications, while repeat post mobilization device checks has low yield of detection of complications. The only statistically significant predictor of peri‐procedural complications is the duration of the procedure; longer procedures were associated with higher rates of complications. [ABSTRACT FROM AUTHOR]

Supervised classifiers require a lot of data with accurate labels to learn to recognize chest X-ray images (CXR). However, manually labeling an extensive collection of CXR images is time-consuming and costly. To address this issue, a method for the semi-supervised labelling of extensive collections of CXR images is proposed leveraging unsupervised clustering with minimum expert knowledge to generate ground truth images. The proposed methodology entails: using unsupervised clustering techniques such as K-Means and Self-Organizing Maps. Second, the images are fed to five different feature vectors to utilize the potential differences between features to their full advantage. Third, each data point gets the label of the cluster’s center to which it belongs. Finally, a majority vote is used to decide the ground truth image. The number of clusters created by the method chosen strictly limits the amount of human involvement. To evaluate the effectiveness of the proposed method, experiments were conducted on two publicly available CXR datasets, namely VinDR-CXR and Montgomery datasets. The experiments showed that, for a KNN classifier, manually labeling only 1% (VinDr- CXR), or 10% (Montgomery) of the training data, gives a similar performance as labeling the whole dataset. The proposed methodology efficiently generates ground-truth images from publicly available CXR datasets. To our knowledge, this is the first study to use the VinDr-CXR and Montgomery datasets for ground truth image generation. Extensive experimental analysis using machine learning and statistical techniques shows that the proposed methodology efficiently generates ground truth images from CXR datasets. [ABSTRACT FROM AUTHOR]