Chest X-ray Images Of Patients Research Articles

Deep Learning to Detect Pulmonary Hypertension from the Chest X-Ray Images of Patients with Systemic Sclerosis.

Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis (SSc). Deep learning models can be applied to detect PH in the chest X-ray images of these patients. The aim of the study was to investigate the performance and prognostic implications of a deep learning algorithm for the diagnosis of PH in SSc patients using chest X-ray images.Chest X-ray images were acquired from 230 SSc patients with suspected PH who underwent chest X-ray and right heart catheterization (RHC). A convolutional neural network was trained to identify the data of patients with PH (mean pulmonary arterial pressure > 20 mmHg). Kaplan-Meier analysis was used to evaluate survival. The area under the receiver operating characteristic curve (AUC) obtained with the deep learning algorithm was 0.826 while the AUC obtained with cardiologist assessments of the same images was 0.804. The 5-year prognosis was 83.4% in patients with PH detected by RHC, and 85% in those with PH detected by the model.The deep learning model developed in this study can detect PH from the chest X-ray data of SSc patients. The prognostic accuracy of the model was demonstrated as well.

Deep Learning-based Lung dose Prediction Using Chest X-ray Images in Non-small Cell Lung Cancer Radiotherapy.

This study aimed to develop a deep learning model for the prediction of V20 (the volume of the lung parenchyma that received ≥20 Gy) during intensity-modulated radiation therapy using chest X-ray images. The study utilized 91 chest X-ray images of patients with lung cancer acquired routinely during the admission workup. The prescription dose for the planning target volume was 60 Gy in 30 fractions. A convolutional neural network-based regression model was developed to predict V20. To evaluate model performance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were calculated with conducting a four-fold cross-validation method. The patient characteristics of the eligible data were treatment period (2018-2022) and V20 (19.3%; 4.9%-30.7%). The predictive results of the developed model for V20 were 0.16, 5.4%, and 4.5% for the R2, RMSE, and MAE, respectively. The median error was -1.8% (range, -13.0% to 9.2%). The Pearson correlation coefficient between the calculated and predicted V20 values was 0.40. As a binary classifier with V20 <20%, the model showed a sensitivity of 75.0%, specificity of 82.6%, diagnostic accuracy of 80.6%, and area under the receiver operator characteristic curve of 0.79. The proposed deep learning chest X-ray model can predict V20 and play an important role in the early determination of patient treatment strategies.

A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images

In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach.

Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

Pneumona Disease Detection using Deep Learning

Abstract: Pneumonia is a life-threatening respiratory disease that causes inflammation and fluid accumulation in the lungs. Chest X-ray is one of the most commonly used diagnostic tools to detect pneumonia. In recent years, deep learning techniques have been widely used in the medical field for image analysis and diagnosis. In this study, we propose a deep learning-based method for pneumonia detection using chest X-ray images. This study proposes a Convolutional Neural Network (CNN) based approach for detecting pneumonia using chest X-ray images. The proposed model consists of multiple layers of convolution, pooling, and fully connected layers, designed to extract features from input images and classify them into pneumonia and nonpneumonia categories. The model is trained on a publicly available dataset containing chest X-ray images of patients with and without pneumonia. The performance of the proposed model is evaluated using various metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The results indicate that the proposed model achieved high accuracy, sensitivity, and specificity in detecting pneumonia from chest X-ray images, demonstrating its potential for clinical use in aiding radiologists in diagnosing and monitoring patients with pneumonia, particularly in cases where the diagnosis is challenging due to factors such as image quality or complex cases. Moreover, the proposed approach can be integrated into existing radiology workflows to automate the detection and classification of chest X-ray images, leading to faster and more accurate diagnoses, thereby improving patient outcomes

Disease Classification Model Based on Multi-Modal Feature Fusion

Aiming at the application of classification tasks in deep learning in disease classification, this paper proposes a disease classification model based on multi-modal feature fusion. In this model, chest X-ray images of patients are used as image modal data, and corresponding disease descriptions are used as text modal data. By innovatively pro-posing an adaptive multi-modal attention mechanism, the feature vectors extracted from the two modal data are fused for classification by classifier. In order to verify the effectiveness of the proposed model in disease classification, this paper uses Chest X-ray dataset in open I database. In order to solve the problem of small sample size and unbalanced sample categories in this dataset, SMOTE algorithm is used to expand the samples and ablation study is designed to compare the model effects. The results show that the model based on both image and text modes and sample expansion by SMOTE algorithm can solve the problems of overfitting, low recall and F1 value due to small samples and unbalanced samples. In addition, the classification accuracy of multi-modal model using image and text is improved by about 0.55% and 2.69% respectively compared with single-modal model using only image or text. Similarly, the addition of adaptive multi-modal attention mechanism also improves the classification effect of the model by about 0.41% compared with the feature fusion method using vector concatenation simply.

Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images.

Chest X-ray (CXR) is indispensable to the assessment of severity, diagnosis, and management of pneumonia. Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. This study investigated the feasibility of classifying fatal pneumonia based on CXR images using deep learning models on publicly available platforms. CXR images of patients with pneumonia at diagnosis were labeled as fatal or nonfatal based on medical records. We applied CXR images from 1031 patients with nonfatal pneumonia and 243 patients with fatal pneumonia for training and self-evaluation of the deep learning models. All labeled CXR images were randomly allocated to the training, validation, and test datasets of deep learning models. Data augmentation techniques were not used in this study. We created two deep learning models using two publicly available platforms. The first model showed an area under the precision-recall curve of 0.929 with a sensitivity of 50.0% and a specificity of 92.4% for classifying fatal pneumonia. We evaluated the performance of our deep learning models using sensitivity, specificity, PPV, negative predictive value (NPV), accuracy, and F1 score. Using the external validation test dataset of 100 CXR images, the sensitivity, specificity, accuracy, and F1 score were 68.0%, 86.0%, 77.0%, and 74.7%, respectively. In the original dataset, the performance of the second model showed a sensitivity, specificity, and accuracy of 39.6%, 92.8%, and 82.7%, respectively, while external validation showed values of 38.0%, 92.0%, and 65.0%, respectively. The F1 score was 52.1%. These results were comparable to those obtained by respiratory physicians and residents. The deep learning models yielded good accuracy in classifying fatal pneumonia. By further improving the performance, AI could assist physicians in the severity assessment of patients with pneumonia.

Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs.

Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images. In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance. The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90). We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.

IMPROVEMENT OF INCEPTIONV3 MODEL CLASSIFICATION PERFORMANCE USING CHEST X-RAY IMAGES

In this study, we attempted to confirm whether the InceptionV3 model, which shows excellent performance in lung disease classification using chest X-ray images, is suitable for cardiac disease classification. In addition, we proposed a method for improving classification accuracy by improving the structure of the existing InceptionV3 model. The deep learning model used in this study was a modified version of the fully-connected hierarchical structure of InceptionV3. The proposed InceptionV3 model structure was constructed to differentiate between a normal heart and hypertrophic heart. The data used for model training were trained after data augmentation on 1026 chest X-ray images of patients diagnosed with normal heart and cardiac hypertrophy at Kyungpook National University Hospital. The experiment showed a learning classification accuracy of 99.57% and loss of 1.42% for the original InceptionV3 model. The accuracy and loss of the modified InceptionV3 model were 99.81% and 0.92%, respectively. Its classification performance was evaluated based on precision, recall, and F1 score. For a normal heart, precision, recall and F1 score were 78%, 100% and 88%, respectively. For cardiomegaly, classification accuracy, recall and F1 score were 78%, 100% and 88%, respectively. Conversely, the modified model showed 100% precision, 92% recall and 96% F1 score. For cardiomegaly, classification accuracy, recall rate and F1 score were 95, 100 and 97%, respectively. In conclusion, better classification can be achieved if the chest X-ray images for a normal heart and cardiomegaly are classified using the proposed model. Hence, the reliability of the classification performance gradually increases.

DEVELOPMENT OF CONVOLUTIONAL NEURAL NETWORK MODEL FOR CLASSIFICATION OF CARDIOMEGALY X-RAY IMAGES

In this study, the effect of the input image format on the accuracy in deep learning (DL) was evaluated using tagged image file format (TIFF), PNG, and JPEG images converted from DICOM images. Based on the evaluation results, a convolutional neural network (CNN)-based DL model was proposed. The training and test data used in this study were chest X-ray images of patients diagnosed with normal heart or cardiomegaly conditions. CNN models, namely VGGNet, ResNet, InceptionNet (GoogleNet), DenseNet, and EfficientNet, were used to compare the results according to the types of input images, that is, images converted from DICOM images into TIFF, PNG, and JPEG images. The classification performance was validated by proposing a CNN model that can classify normal hearts and cardiomegaly in chest X-ray images. In this study, through medical imaging research using deep learning, it was demonstrated that the classification performance was unaffected even when the DICOM image was converted into any format and used as an input image. In addition, the proposed CNN model exhibited excellent performance in classifying normal hearts and cardiomegaly on X-ray images. This can be used in various studies that aim to apply DL to medical images by providing information according to the input image type and is also considered to aid in the selection of image type and learning parameters. The proposed model is expected to yield useful results for the classification of diseases in chest X-ray images.

CLASSIFICATION OF CORONAVIRUS DISEASE (COVID-19) THROUGH CHEST X-RAY IMAGES BASED ON DEEP LEARNING

CoV-2 virus this disease is spreading rapidly throughout the world. Various studies were carried out to control the spread of Covid-19. One way to detect Covid-19 is to study chest X-ray images of patients with Covid-19 symptoms. However, to detect Covid-19 through x-ray images, there are currently few radiology specialists needed. This study researched to detection of Covid-19 disease through chest x-ray images with a deep learning approach based on a convolutional neural network (CNN). Before training the model, data preprocessing is carried out, such as labeling and resizing. This study uses a CNN model with three layers of convolution and max-pooling layers and a fully-connected layer for the output. The results of the training using the CNN method produced a pretty good performance, with the best training accuracy (acc) value obtained in the 31st epoch with a value of 0.9593, training loss (loss) 0.1306, validation accuracy (val_acc) 0.9604, and loss validation (val_loss). 0.1399.

Coronavirus Detection using Deep Pre-trained Model on Chest X-ray Imaging

The constant increase in the number of patients suffering from the pandemic Covid-19 has overpowered the medical management system over the world. This pandemic has affected every aspect of human life all over the world. However, the limited number of testing kits is making it more challenging to deal with the global pandemic. It is near to impossible for people to maintain social distancing all the time, and lengthy testing procedure is one of the major cause of the rapid spread of the epidemic Covid-19. The polymerase chain reaction test that is done in real-time (RT-PCR) is time-consuming for each patient with gasping disease to be tested. However, the use of a chest x-ray machine tool can accelerate testing time. X-ray machines are now accessible in almost every medical management healthcare system, with instant results. The proposed research aims to detect the pandemic. The dataset was collected from an open-source repository that consists of the chest x-ray images of patients who were Covid-19 positive, and covid-19 negative which means not infected by the pandemic coronavirus but suffering from normal pneumonia. The approach of this paper uses a deep convolutional neural network model i-e VGG16, to begin the process of classifying images into positive or negative. In the proposed research VGG16, the Deep Convolutional Network model is used, this is a pre-trained model, previously trained on the ImageNet dataset that com-prises 14M images that belong to 1K different classes. This model is fine-tuned on 1202 images. The preprocessing of collected data is done by converting it into an RGB channel and resizing it to match the requirement of our model i-e VGG16. Data augmentation is performed on the preprocessed dataset. Our proposed model gives 99.1%, 100%, and 98.59% accuracies, sensitivity specificity respectively for the detection of Covid-19 on patients’ cxr images.

Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients

COVID-19 is now regarded as the most lethal disease caused by the novel coronavirus disease of humans. The COVID-19 pandemic has spread to every country on the planet and has wreaked havoc on these countries by increasing the number of human deaths, and in addition, caused intense hunger, and lowered economic productivity. Due to a lack of sufficient radiologist, a restricted amount of COVID-19 test kits is available in hospitals, and this is also accompanied by a shortage of equipment due to the daily increase in cases, as a result of increase in the number of persons infected with COVID-19 . Even for experienced radiologists, examining chest X-rays is a difficult task. Many people have died as a result of inaccurate COVID-19 diagnosis and treatment, as well as ineffective detection measures. This paper, therefore presents a unique detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients. To achieve quick diagnosis, a convolutional neural network (CNN) and histogram of oriented gradients (HOG) method is proposed in this paper to help medical experts diagnose COVID-19 disease. The diagnostic performance of the hybrid CNN model and HOG-based method was then evaluated using chest X-ray images collected from University of Gondar and online databases. The experiment was performed using Keras (with TensorFlow as a backend) and Python. After the DCCNet model was evaluated, a 99.9% training accuracy and 98.3% test accuracy was achieved, while a 100% training accuracy and 98.5% test accuracy was achieved using HOG. After the evaluation, the hybrid model achieved 99.97% and 99.67% training and testing accuracy for detection and classification of COVID-19 which was better by 1.37% compared to when features were extracted using CNN and 1.17% when HOG was used. The DCCNet achieved a result that outperformed state-of-the-art models by 6.7%.

An efficient image encryption scheme for healthcare applications.

In recent years, there has been an enormous demand for the security of image multimedia in healthcare organizations. Many schemes have been developed for the security preservation of data in e-health systems however the schemes are not adaptive and cannot resist chosen and known-plaintext attacks. In this contribution, we present an adaptive framework aimed at preserving the security and confidentiality of images transmitted through an e-healthcare system. Our scheme utilizes the 3D-chaotic system to generate a keystream which is used to perform 8-bit and 2-bit permutations of the image. We perform pixel diffusion by a key-image generated using the Piecewise Linear Chaotic Map (PWLCM). We calculate an image parameter using the pixels of the image and perform criss-cross diffusion to enhance security. We evaluate the scheme’s performance in terms of histogram analysis, information entropy analysis, statistical analysis, and differential analysis. Using the scheme, we obtain the average Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values for an image of size 256 × 256 equal to 99.5996 and 33.499 respectively. Furthermore, the average entropy is 7.9971 and the average Peak Signal to Noise Ratio (PSNR) is 7.4756. We further test the scheme on 50 chest X-Ray images of patients having COVID-19 and viral pneumonia and found the average values of variance, PSNR, entropy, and Structural Similarity Index (SSIM) to be 257.6268, 7.7389, 7.9971, and 0.0089 respectively. Furthermore, the scheme generates completely uniform histograms for medical images which reveals that the scheme can resist statistical attacks and can be applied as a security framework in AI-based healthcare.

COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images.

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques.

Since the arrival of the novel Covid-19, several types of researches have been initiated for its accurate prediction across the world. The earlier lung disease pneumonia is closely related to Covid-19, as several patients died due to high chest congestion (pneumonic condition). It is challenging to differentiate Covid-19 and pneumonia lung diseases for medical experts. The chest X-ray imaging is the most reliable method for lung disease prediction. In this paper, we propose a novel framework for the lung disease predictions like pneumonia and Covid-19 from the chest X-ray images of patients. The framework consists of dataset acquisition, image quality enhancement, adaptive and accurate region of interest (ROI) estimation, features extraction, and disease anticipation. In dataset acquisition, we have used two publically available chest X-ray image datasets. As the image quality degraded while taking X-ray, we have applied the image quality enhancement using median filtering followed by histogram equalization. For accurate ROI extraction of chest regions, we have designed a modified region growing technique that consists of dynamic region selection based on pixel intensity values and morphological operations. For accurate detection of diseases, robust set of features plays a vital role. We have extracted visual, shape, texture, and intensity features from each ROI image followed by normalization. For normalization, we formulated a robust technique to enhance the detection and classification results. Soft computing methods such as artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier, and deep learning classifier are used for classification. For accurate detection of lung disease, deep learning architecture has been proposed using recurrent neural network (RNN) with long short-term memory (LSTM). Experimental results show the robustness and efficiency of the proposed model in comparison to the existing state-of-the-art methods.

Covidense: Providing a Suitable Solution for Diagnosing Covid-19 Lung Infection Based on Deep Learning from Chest X-Ray Images of Patients

Purpose: Coronavirus disease 2019 (Covid-19), first reported in December 2019 in Wuhan, China, has become a pandemic. Chest imaging is used for the diagnosis of Covid-19 patients and can address problems concerning Reverse Transcription-Polymerase Chain Reaction (RT-PCR) shortcomings. Chest X-ray images can act as an appropriate alternative to Computed Tomography (CT) for diagnosing Covid-19. The purpose of this study is to use a Deep Learning method for diagnosing Covid-19 cases using chest X-ray images. Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral pneumonia cases. Materials and Methods: In this study, a total number of 1280 chest X-ray images of Covid-19, normal, bacterial and viral pneumonia cases were collected from open access repositories. Covidense, a convolutional neural network model, is based on the pre-trained DenseNet-201 architecture, and after pre-processing the images, it has been trained and tested on the images using the 5-fold cross-validation method. Results: The accuracy of different classifications including classification of two classes (Covid-19, normal), three classes 1 (Covid-19, normal and bacterial pneumonia), three classes 2 (Covid-19, normal and viral pneumonia), and four classes (Covid-19, normal, bacterial pneumonia and viral pneumonia) are 99.46%, 92.86%, 93.91 %, and 91.01% respectively. Conclusion: This model can differentiate pneumonia caused by Covid-19 from other types of pneumonia, including bacterial and viral. The proposed model offers high accuracy and can be of great help for effective screening. Thus, reducing the rate of infection spread. Also, it can act as a complementary tool for the detection and diagnosis of Covid-19.

A Detailed Study on the Choice of Hyperparameters for Transfer Learning in Covid-19 Image Datasets using Bayesian Optimization

For many years, the area of health care has evolved, mainly using medical images to detect and evaluate diseases. Nowadays, the world is going through a pandemic due to COVID- 19, causing a severe effect on the health system and the global economy. Researchers, both in health and in different areas, are focused on improving and providing various alternatives for rapid and more effective detection of this disease. The main objective of this study is to automatically explore as many configurations as possible to recommend a smaller starting hyperparameter space. Because the manual selection of these hyperparameters can lose configurations that generate more efficient models, for this, we present the MKCovid-19 workflow, which uses chest x-ray images of patients with COVID-19. We use knowledge transfer based on convolutional neural networks and Bayes optimization. A detailed study was conducted with different amounts of training data. This automatic selection of hyperparameters allowed us to find a robust model with an accuracy of 98% in test data.

Comparison of the conventional reusable cotton and disposable nonwoven fabric patient gowns in digital chest radiography

The purpose of this study was to analyze artifacts caused by conventional cotton material gowns in chest X-ray images obtained with digital radiography (DR) and to evaluate whether image artifacts were reduced when nonwoven fabric gowns were used. We assessed DR chest X-ray images of patients in our hospital database for artifacts. We also obtained images of reusable woven gowns made of two thicknesses of cotton and of disposable gowns made of unwoven polypropylene material. We then measured signal and noise obtained in the same test regions of these different types of gowns, three times under the same conditions and calculated the mean of each signal. The regions of interest (ROI) selected were based on three representative regions of the chest images; signal and noise were measured using a window width of 950 and a window level of 3340 after setting an ROI measuring 334 mm2. In the patient images, linear artifacts caused by cotton gowns appeared in the left and right upper lung areas, while no artifacts were caused by gowns made of nonwoven material. In the comparison of gowns made of different materials, the mean value of the ROI signal of the cotton material was significantly higher than that of the nonwoven material (p = 0.01); the noise value of the cotton material was also higher, but without statistical significance. Linear artifacts were found in images obtained in DR chest radiography when patients wore cotton fabric gowns, but no artifacts were observed when these were replaced with nonwoven fabric gowns.