Chest X-ray Scan Research Articles

1426. Characterization of Patients with Iatrogenic Mycobacterial Infections: A Hospital-Based Survey

Abstract Background Tuberculosis (TB) is a chronic granulomatous inflammation usually involving the lung parenchyma and hilar lymph nodes. Extra-pulmonary involvement is seen in ∼20% of all TB cases. Developing tuberculosis following treatment for another primary medical condition is a rare occurrence. Iatrogenic literally means illness caused by medical examination or treatment. Mycobacteria have been used to treat a few medical conditions and the therapeutic use has been validated extensively in literature. The mycobacteria used for therapeutic purposes are supposed to be either attenuated or non pathogenic strains. Growth and dissemination of this mycobacterium is a rare but serious possibility. Reactivation of latent mycobacterial infection following therapy is also a part of iatrogenic mycobacterial infection. Methods We report a retrospective study investigating adverse events manifesting with development of iatrogenic tuberculosis. The data was obtained from a tertiary care centre over a period of 2 years. Diagnosis of tuberculosis was established based on clinical, radiological(chest X-Ray and High resolution CT scan), sputum smear microscopy and skin/tissue biopsy and Xpert MTB/RIF. Tuberculin skin test(TST) and Interferon gamma release assay (IGRA) were used as additional diagnostic tests. Results The search yielded 26 cases of iatrogenic tuberculosis, with a median age of 56.5 years. Most common cause was reactivation of latent tuberculosis due to use of anti-TNF alpha biologic agents(53.8%). Development of Tuberculosis verrucosa cutis following BCG inoculation for verruca vulgaris was noted in about 19.2% of cases and Tubercular abscess due to Mycobacterium w or BCG inoculation for Covid-19 was noted in 11.5% cases. Bacillus Calmette Guérin(BCG) induced balanitis secondary to therapy for Carcinoma urinary bladder was noted in 7.69%of cases. Bacillus Calmette-Guerin or BCG induced balanitis following use of intravesical immunotherapy for treating early-stage urinary bladder cancer and successful clearance of lesions post therapy Tuberculosis Verrucosa Cutis following Immunotherapy for plantar warts Conclusion All patients were managed successfully with anti tubercular therapy. These observations indicate that tuberculosis infection can develop in previously healthy individuals in endemic zones following biologic therapy of use of mycobacterial agents for other therapeutic indications. Disclosures All Authors: No reported disclosures.

Pneumonia of Chest X-Ray Images Detection using VGG Architectures

Pneumonia is one of the world's most frequent diseases. Pneumonia, a serious respiratory infection, damages the lungs and makes it difficult to breathe. Chest X-ray scans are one of the most significant and common procedures for diagnosing pneumonia condition. An effective model for the identification of pneumonia based on digital chest X-ray pictures is proposed in this paper, which could help radiologists make better decisions. This is a supervised learning technique in which the proposed model predicts the outcome depending on the dataset's image quality. In this research, Kaggle dataset has been used to train the model. To improve training and validation accuracy, fine-tuning of Visual Geometry Group (VGG) pre-trained model are conducted with different hyperparameters. The deep Convolutional Neural Networks (CNNs) based VGG-16 and VGG-19 architectures are used to extract features from given chest X-ray images. These features are then used for classification. In addition to the accuracy and F1-score as an evaluation matrix, the results of pre-trained models are compared using the model loss, and model accuracy graphs. The model's performance in detecting pneumonia demonstrates that the proposed VGG pre-trained model can efficiently categorize normal and pneumonia chest X-rays images in practice. As a result, the proposed model can be utilized to make a speedy diagnosis of pneumonia and can assist radiologists with their work.

Anaesthetic management of a huge mediastinal tumour with tracheobronchial compression

A 20 year old man diagnosed to have multiple neurofibromatosis presented with 2 months history of orthopnoea and cough with moderate expectoration since 1 month. Chest Xray and CT scan showed a huge mediastinal mass with compression of the trachea and superior vena cava. In view of the anticipated airway problems, an awake intubation in the sitting position was achieved. Cardiopulmonary bypass was not considered because of the possibility of excessive bleeding after heparinization. Despite this, the patient bled about 4,500 mL. Tracheomalacia was suspected intraoperatively. A larger endotracheal tube was inserted at the end of the procedure and the patient ventilated electively overnight. He was successfully extubated 24 hours later.

Wavelet subband discriminator for efficient unsupervised chest X-ray image restoration.

Chest X-ray (CXR) images are commonly used to show the internal structure of the human body without invasive intervention. The quality of CXR is an important factor as it affects the accuracy of a clinical diagnosis. Unfortunately, it is difficult to always get good quality CXR scans due to noises andscatters. Recently, wavelet directional CycleGAN (WavCycleGAN) has shown promising results in image restoration tasks by removing noise and artifacts without sacrificing high-frequency components of the input image. Unfortunately, WavCycleGAN directly reconstructs wavelet directional images that require a wavelet transform in both the training and test phases, resulting in additional processing steps and unnatural artifacts originating from the wavelet domain image. In addition, WavCycleGAN can only process artifact-related subbands, so it is difficult to apply WavCycleGAN when different levels of artifacts are present in all subbands. To address this, here we present a novel unsupervised CXR image restoration scheme with similar or even better artifact removal performance than WavCycleGAN in spite of wavelet transform being only applied in the trainingphase. We introduce a novel wavelet subband discriminator which can be combined with CycleGAN or switchable CycleGAN, where wavelet transform is applied only in the training phase for discriminators to match the distribution of wavelet subband components. In our framework, the image restoration network can be still applied in the image domain to prevent unnatural artifacts of the wavelet domain image with the help of the image-domain cycle-consistency loss. In addition, using wavelet subband discriminator makes it possible to remove artifacts in all subbands by utilizing frequency-specific wavelet subbanddiscriminators. Through extensive experiments for noise and scatter removal in CXRs, we confirm that our method provides competitive performance compared to existing approaches without additional processing steps in the test phase. Furthermore, we show that our wavelet subband discriminator combined with the switchable CycleGAN can provide the flexibility by generating different levels of artifact removal. The proposed wavelet subband discriminator can be combined with the existing CycleGAN or switchable CycleGAN structures to construct an efficient unsupervised CXR image reconstruction. The advantage of our wavelet subband discriminator-based CXR image restoration is that, unlike traditional WavCycleGAN, it does not require any additional processing steps in the testing phase and does not generate unnatural artifacts originating from the wavelet domain image. We believe that our wavelet subband discriminator can be applied to various CXR imageapplications.

DETECTION OF COVID-19 WITH CHEST X-RAY

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

Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID-19 Early Diagnostics from X-ray Images

Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic since its emergence. One of the most important ways to control the spread of this disease begins with early detection, which allows that isolation and treatment could perhaps be started. According to recent results, chest X-ray scans provide important information about the onset of the infection, and this information may be evaluated so that diagnosis and treatment can begin sooner. This is where artificial intelligence collides with skilled clinicians’ diagnostic abilities. The suggested study’s goal is to make a contribution to battling the worldwide epidemic by using a simple convolutional neural network (CNN) model to construct an automated image analysis framework for recognizing COVID-19 afflicted chest X-ray data. To improve classification accuracy, fully connected layers of simple CNN were replaced by the efficient extreme gradient boosting (XGBoost) classifier, which is used to categorize extracted features by the convolutional layers. Additionally, a hybrid version of the arithmetic optimization algorithm (AOA), which is also developed to facilitate proposed research, is used to tune XGBoost hyperparameters for COVID-19 chest X-ray images. Reported experimental data showed that this approach outperforms other state-of-the-art methods, including other cutting-edge metaheuristics algorithms, that were tested in the same framework. For validation purposes, a balanced X-ray images dataset with 12,000 observations, belonging to normal, COVID-19 and viral pneumonia classes, was used. The proposed method, where XGBoost was tuned by introduced hybrid AOA, showed superior performance, achieving a classification accuracy of approximately 99.39% and weighted average precision, recall and F1-score of 0.993889, 0.993887 and 0.993887, respectively.

Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification

Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-based framework for the detection of COVID-19 status from chest X-ray and CT scan imaging data acquired from three benchmark imagery datasets. VGG-19, ResNet-50 and Inception-V3 models are employed in this research study to perform image classification. A variety of evaluation metrics including kappa statistic, Root-Mean-Square Error (RMSE), accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Recall, precision, and F-measure are used to ensure adequate performance of the proposed framework. Our findings indicate that the Inception-V3 model has the best performance in terms of COVID-19 status detection.

A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis

This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.

Lung Lobe Segmentation and Feature Extraction-Based Hierarchical Attention Network for COVID-19 Prediction from Chest X-Ray Images

AbstractCoronavirus disease 2019 (COVID-19) is a rising respiratory sickness. It causes harsh pneumonia and is considered to cover higher collisions in the healthcare domain. The diagnosis at an early stage is more complex to get accurate treatment for reducing the stress in the clinical sector. Chest X-ray scan is the standard imaging diagnosis test employed for pneumonia disease. Automatic detection of COVID-19 helps to control the community outbreak but tracing this viral infection through X-ray results in a challenging task in the medical community. To automatically detect the viral disease in order to reduce the mortality rate, an effective COVID-19 detection method is modelled in this research by the proposed manta-ray multi-verse optimization-based hierarchical attention network (MRMVO-based HAN) classifier. Accordingly, the MRMVO is the incorporation of manta-ray foraging optimization and multi-verse optimizer. Based on the segmented lung lobes, the features are acquired from segmented regions in such a way that the process of COVID-19 detection mechanism is carried out with the features acquired from interested lobe regions. The proposed method has good performance with the measures, such as accuracy, true positive rate and true negative rate with the values of 93.367, 89.921 and 95.071%.

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

AML-199 Radiological Aspects of Pneumonias in Neutropenic Patients

Imaging, such as chest X-ray and chest CT scan, plays a pioneering role in the diagnosis of neutropenic patients with respiratory dysfunction. To analyze radiographic patterns of adults with prolonged neutropenia and pneumonia. An observational, descriptive, and prospective study that analyzed patients admitted to the protected unit (acute myeloid leukemia and aplastic anemia) and transplant unit of Hematology and Pediatric Oncology at August 20th Hospital in Casablanca, from May 1st, 2019 to February 29th, 2020. Patients included were 18 years of age or older receiving chemotherapy and then developed respiratory symptoms during the period of neutropenia. An exploitation form was used to acquire the data then its analysis was performed with IBM SPSS Statistics version 21. Chest X-rays were taken during 20 of the 50 episodes of pneumonia (40%). They were all pathological, including alveolar opacities (90%), excavation (5%), reticulonodular (15%), and effusion syndrome fluid (15%). Chest computed tomography (CT scan) was performed in all patients. The time of its realization from the appearance of signs call clinics was variable ranging from 0 to 8 days, with a median of 2 days. It is important to note that distinct picture radiographs can coexist on the same X-ray and in the same patient. Forty six percent of our patients had condensation hearth and micronodules, nodules (58%), ground-glass opacity (60%), fluid effusion (34%), mediastinal lymphadenopathy (30%), interstitial thickening (22%), and 16% had reticulonodular infiltrates. Eight patients (16%) in our study had Halo sign, 10% had cavity image as well as Atelectasis, 2% with tree-in-bud aspect, and 2% had centrilobular emphysema. Thoracic CT improves the quality of the diagnosis; it also contributes to better management of neutropenic patients when pneumonia is suspected.

COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers.

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.

Unsupervised Deep learning-based Feature Fusion Approach for Detection and Analysis of COVID-19 using X-ray and CT Images

Aims: This study investigates an unsupervised deep learning-based feature fusion approach for the detection and analysis of COVID-19 using chest X-ray (CXR) and Computed tomography (CT) images. Background: The outbreak of COVID-19 has affected millions of people all around the world and the disease is diagnosed by the reverse transcription-polymerase chain reaction (RT-PCR) test which suffers from a lower viral load, and sampling error, etc. Computed tomography (CT) and chest X-ray (CXR) scans can be examined as most infected people suffer from lungs infection. Both CT and CXR imaging techniques are useful for the COVID-19 diagnosis at an early stage and it is an alternative to the RT-PCR test. Objective: The manual diagnosis of CT scans and CXR images are labour-intensive and consumes a lot of time. To handle this situation, many AI-based solutions are researched including deep learning-based detection models, which can be used to help the radiologist to make a better diagnosis. However, the availability of annotated data for COVID-19 detection is limited due to the need for domain expertise and expensive annotation cost. Also, most existing state-of-the-art deep learning-based detection models follow a supervised learning approach. Therefore, in this work, we have explored various unsupervised learning models for COVID-19 detection which does not need a labelled dataset. Methods: In this work, we propose an unsupervised deep learning-based COVID-19 detection approach that incorporates the feature fusion method for performance enhancement. Four different sets of experiments are run on both CT and CXR scan datasets where convolutional autoencoders, pre-trained CNNs, hybrid, and PCA-based models are used for feature extraction and K-means and GMM techniques are used for clustering. Results: The maximum accuracy of 84% is achieved by the model Autoencoder3-ResNet50 (GMM) on the CT dataset and for the CXR dataset, both Autoencoder1-VGG16 (KMeans and GMM) models achieved 70% accuracy. Conclusion: Our proposed deep unsupervised learning, feature fusion-based COVID-19 detection approach achieved promising results on both datasets. It also outperforms four well-known existing unsupervised approaches.

Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach.

COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.

A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

The novel coronavirus (COVID-19) is a contagious viral disease that has rapidly spread worldwide since December 2019, causing the disruption of life and heavy economic losses. Since the beginning of the virus outbreak, a polymerase chain reaction has been used to detect the virus. However, since it is an expensive and slow method, artificial intelligence researchers have attempted to develop quick, inexpensive alternative methods of diagnosis to help doctors identify positive cases. Therefore, researchers are starting to incorporate chest X-ray scans (CXRs), an easy and inexpensive examination method. This study used an approach that uses image cropping methods and a deep learning technique (updated VGG16 model) to classify three public datasets. This study had four main steps. First, the data were split into training and testing sets (70% and 30%, respectively). Second, in the image processing step, each image was cropped to show only the chest area. The images were then resized to 150 × 150. The third step was to build an updated VGG16 convolutional neural network (VGG16-CNN) model using multiple classifications (three classes: COVID-19, normal, and pneumonia) and binary classification (COVID-19 and normal). The fourth step was to evaluate the model’s performance using accuracy, sensitivity, and specificity. This study obtained 97.50% accuracy for multiple classifications and 99.76% for binary classification. The study also got the best COVID-19 classification accuracy (99%) for both models. It can be considered that the scientific contribution of this research is summarized as: the VGG16 model was reduced from approximately 138 million parameters to around 40 million parameters. Further, it was tested on three different datasets and proved highly efficient in performance.

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

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

Development of a Bronchoscopy-Radiologic Skills and Task Assessment Tool (BRadSTAT): A Tool for Evaluating the Radiological Skills of Bronchoscopists with Different Experience

Background: Competency using radiologic images for bronchoscopic navigation is presumed during subspecialty training, but no assessments objectively measure combined knowledge of radiologic interpretation and ability to maneuver a bronchoscope into peripheral airways. Objectives: The objectives of this study were (i) to determine whether the Bronchoscopy-Radiology Skills and Tasks Assessment Tool (BRadSTAT) discriminates between bronchoscopists of various levels of experience and (ii) to improve construct validity using study findings. Methods: BRadSTAT contains 10 questions that assess chest X-ray and CT scan interpretation using multiple images per question and 2 technical skill assessments. After administration to 33 bronchoscopists (5 Beginners, 9 Intermediates, 10 Experienced, and 9 Experts), discriminative power was strengthened using differential weighting on CT-related questions, producing the BRadSTAT-CT score. Cut points for both scores were determined via cross-validation. Results: Mean BRadSTAT scores for Beginner, Intermediate, Experienced, and Expert were 74 (±13 SD), 78 (±14), 86 (±9), and 88 (±8), respectively. Statistically significant differences were noted between Expert and Beginner, Expert and Intermediate, and Experienced and Beginner (all p ≤ 0.05). Mean BRadSTAT-CT scores for Beginner, Intermediate, Experienced, and Expert were 63 (±14), 74 (±15), 82 (±13), and 90 (±9), respectively, all statistically significant (p ≤ 0.03). Cut points for BRadSTAT-CT had lower sensitivity but greater specificity and accuracy than for BRadSTAT. Conclusion: BRadSTAT represents the first validated assessment tool measuring knowledge and skills for bronchoscopic access to peripheral airways, which discriminates between bronchoscopists of various experience levels. Refining BRadSTAT produced the BRadSTAT-CT, which had higher discriminative power. Future studies should focus on their usefulness in competency-based bronchoscopy programs.

Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray

Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used a machine learning model to differentiate between COVID-19 and pneumonia via radiomic features using a bias-minimized dataset of chest X-ray scans. We used logistic regression (LR), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), bagging, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) to differentiate between COVID-19 and pneumonia based on training data. Further, we used a grid search to determine optimal hyperparameters for each machine learning model and 5-fold cross-validation to prevent overfitting. The identification performances of COVID-19 and pneumonia were compared with separately constructed test data for four machine learning models trained using the maximum probability, contrast, and difference variance of the gray level co-occurrence matrix (GLCM), and the skewness as input variables. The LGBM and bagging model showed the highest and lowest performances; the GLCM difference variance showed a high overall effect in all models. Thus, we confirmed that the radiomic features in chest X-rays can be used as indicators to differentiate between COVID-19 and pneumonia using machine learning.

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans.

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.

Comparative estimation of pleural effusion volume based on lateral decubitus position of chest x-ray and CT scan imaging

Previous study using thoracic phantom for estimating fluid volume has been obtained which represents the case of pleural effusion based on the size of the x-ray radiograph. The models are obtained in the form of three equations, the pleural effusion volume as a function of height, length times the height, and area of the radiograph image. The three models of estimation have high linearity with ratio value more than 0.988, higher than the modelling measurement using ultrasonography modality. The modelling is expected to give a contribution on developing method for helping clinicians estimate the pleural effusion volume as a basic for performing fluid aspiration and to monitor the therapy. However, because modelling is developed using phantoms, then to be applied clinically, further research is needed for its application to patients. The height function model yields correlation value of 0.966 and paired T-test value of 0.892. The height times length function model yields correlation value of 0.982 and paired T-test value of 0.611. The area function model yields correlation value of 0.997 and paired T-test value of 0.647. From the three equations, measurement of estimated pleural effusion volume using area function on chest x-ray lateral decubitus position is the most appropriate equation. Corresponding to the results of the measurement of gold standard using a CT scan. Height measurement is the measurement that is the fastest and easiest in the application. Limitation of the study is it only can be done in right lateral decubitus position of the patient, and also cannot be applied to patients with condition such as post lung surgery, massive subpulmonic/ supradiaphragmatic pleural effusion, empyema, an atypical pleural effusion such as septated, encapsulated, loculated pleural effusion and anatomical deformity, scoliosis, or abnormalities of thoracic cavity.