Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study

Deep Learning-Based System Combining Chest X-Ray and Computerized Tomography Images for COVID-19 Diagnosis

Aims/Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using deep learning techniques, further improving diagnostic accuracy by using a combined imaging approach. Methods: The study used two publicly accessible databases, COVID-19 Questionnaires for Understanding the Exposure (COVID-QU-Ex) and Integrated Clinical and Translational Cancer Foundation (iCTCF), containing CXR and CT images, respectively. The proposed system employed convolutional neural networks (CNNs) for classification, specifically EfficientNet and ResNet architectures. The data underwent preprocessing steps, including image resizing, Gaussian noise addition, and data augmentation. The dataset was divided into training, validation, and test sets. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability. Results: The EfficientNet-based models outperformed the ResNet-based models across all metrics. The highest accuracy achieved was 99.44% for CXR images and 99.81% for CT images with EfficientNetB5. The models also demonstrated high precision, recall, and F1 scores. For statistical significance, the p-values were less than 0.05, indicating that the results are significant. Conclusion: Integrating CXR and CT images using deep learning significantly improves the accuracy of COVID-19 diagnosis. The EfficientNet-based models, with their superior feature extraction capabilities, show better performance than ResNet models. Grad-CAM Visualizations provide insights into the model's decision-making process, potentially reducing diagnostic errors and accelerating diagnosis processes. This approach can improve patient care and support healthcare systems in managing the pandemic more effectively.

Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study

BACKGROUND

Total hip replacement (THR) is considered the gold standard of treatment for refractory degenerative hip disorders. Identifying patients who should receive THR in the short term is important. Some conservative treatments, such as intra-articular injection administered a few months before THR, may result in higher odds of arthroplasty infection. Delayed THR after functional deterioration may result in poorer outcomes and longer waiting times for those who have been flagged as needing THR. Deep learning (DL) in medical imaging applications has recently obtained significant breakthroughs. However, the use of DL in practical wayfinding, such as short-term THR prediction, is still lacking.

OBJECTIVE

In this study, we will propose a DL-based assistant system for patients with pelvic radiographs to identify the need for THR within 3 months.

METHODS

We developed a convolutional neural network-based DL algorithm to analyze pelvic radiographs, predict the hip region of interest (ROI), and determine whether or not THR is required. The data set was collected from August 2008 to December 2017. The images included 3013 surgical hip ROIs that had undergone THR and 1630 nonsurgical hip ROIs. The images were split, using split-sample validation, into training (n=3903, 80%), validation (n=476, 10%), and testing (n=475, 10%) sets to evaluate the algorithm performance.

RESULTS

The algorithm, called SurgHipNet, yielded an area under the receiver operating characteristic curve of 0.994 (95% CI 0.990-0.998). The accuracy, sensitivity, specificity, and F1-score of the model were 0.977, 0.920, 0932, and 0.944, respectively.

CONCLUSIONS

The proposed approach has demonstrated that SurgHipNet shows the ability and potential to provide efficient support in clinical decision-making; it can assist physicians in promptly determining the optimal timing for THR.

Automated semantic lung segmentation in chest CT images using deep neural network

Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed tomography (CT) images with DeepLabV3 + networks for two-class (background and lung field) and four-class (ground-glass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the DeepLabV3 + network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and ResNet-50. A publicly available database for COVID-19 that contains 750 chest CT images and corresponding pixel-labeled images are used to develop the deep learning model. The segmentation performance has been assessed using five performance measures: Intersection of Union (IoU), Weighted IoU, Balance F1 score, pixel accu-racy, and global accuracy. The experimental results of this work confirm that the DeepLabV3 + network with ResNet-18 and a batch size of 8 have a higher performance for two-class segmentation. DeepLabV3 + network coupled with ResNet-50 and a batch size of 16 yielded better results for four-class segmentation compared to other pretrained networks. Besides, the ResNet with a fewer number of layers is highly adequate for developing a more robust lung segmentation network with lesser computational complexity compared to the conventional DeepLabV3 + network with Xception. This present work proposes a unified DeepLabV3 + network to delineate the two and four different regions automatically using CT images for CoVID-19 patients. Our developed automated segmented model can be further developed to be used as a clinical diagnosis system for CoVID-19 as well as assist clinicians in providing an accurate second opinion CoVID-19 diagnosis.

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems.

CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model

The competence of machine learning approaches to carry out clinical expertise tasks has recently gained a lot of attention, particularly in the field of medical-imaging examination. Among the most frequently used clinical-imaging modalities in the healthcare profession is chest radiography, which calls for prompt reporting of the existence of potential anomalies and illness diagnostics in images. Automated frameworks for the recognition of chest abnormalities employing X-rays are being introduced in health departments. However, the reliable detection and classification of particular illnesses in chest X-ray samples is still a complicated issue because of the complex structure of radiographs, e.g., the large exposure dynamic range. Moreover, the incidence of various image artifacts and extensive inter- and intra-category resemblances further increases the difficulty of chest disease recognition procedures. The aim of this study was to resolve these existing problems. We propose a deep learning (DL) approach to the detection of chest abnormalities with the X-ray modality using the EfficientDet (CXray-EffDet) model. More clearly, we employed the EfficientNet-B0-based EfficientDet-D0 model to compute a reliable set of sample features and accomplish the detection and classification task by categorizing eight categories of chest abnormalities using X-ray images. The effective feature computation power of the CXray-EffDet model enhances the power of chest abnormality recognition due to its high recall rate, and it presents a lightweight and computationally robust approach. A large test of the model employing a standard database from the National Institutes of Health (NIH) was conducted to demonstrate the chest disease localization and categorization performance of the CXray-EffDet model. We attained an AUC score of 0.9080, along with an IOU of 0.834, which clearly determines the competency of the introduced model.

Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.

A deep learning approach for classification of COVID and pneumonia using DenseNet-201

In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.

CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network

The use of computer-assisted analysis to improve image interpretation has been a long-standing challenge in the medical imaging industry. In terms of image comprehension, Continuous advances in AI (Artificial Intelligence), predominantly in DL (Deep Learning) techniques, are supporting in the classification, Detection, and quantification of anomalies in medical images. DL techniques are the most rapidly evolving branch of AI, and it's recently been successfully pragmatic in a variety of fields, including medicine. This paper provides a classification method for COVID 19 infected X-ray images based on new novel deep CNN model. For COVID19 specified pneumonia analysis, two new customized CNN architectures, CVD-HNet1 (COVID-HybridNetwork1) and CVD-HNet2 (COVID-HybridNetwork2), have been designed. The suggested method utilizes operations based on boundaries and regions, as well as convolution processes, in a systematic manner. In comparison to existing CNNs, the suggested classification method achieves excellent Accuracy 98 percent, F Score 0.99 and MCC 0.97. These results indicate impressive classification accuracy on a limited dataset, with more training examples, much better results can be achieved. Overall, our CVD-HNet model could be a useful tool for radiologists in diagnosing and detecting COVID 19 instances early.

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate.

Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques

Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.

Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet

The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4).

Deep learning applied to automatic disease detection using chest X-rays

Deep learning (DL) has shown rapid advancement and considerable promise when applied to the automatic detection of diseases using CXRs. This is important given the widespread use of CXRs across the world in diagnosing significant pathologies, and the lack of trained radiologists to report them. This review article introduces the basic concepts of DL as applied to CXR image analysis including basic deep neural network (DNN) structure, the use of transfer learning and the application of data augmentation. It then reviews the current literature on how DNN models have been applied to the detection of common CXR abnormalities (e.g. lung nodules, pneumonia, tuberculosis and pneumothorax) over the last few years. This includes DL approaches employed for the classification of multiple different diseases (multi-class classification). Performance of different techniques and models and their comparison with human observers are presented. Some of the challenges facing DNN models, including their future implementation and relationships to radiologists, are also discussed.