Identification and Localization of COVID-19Abnormalities on Chest Radiographs using Computer Vision

The Corona Virus (COVID 19) pandemic is quickly becoming the world's most deadly disease. The spreading rate is higher and the early detection helps in faster recovery. The existence of COVID 19 in individuals shall be detected using molecular analysis or through radiographs of lungs. As time and test kit are limited RT- PCR is not suitable to test all. The RT- PCR being a time-consuming process, diagnosis using chest radiographs needs no transportation as the modern X-ray systems are digitized. Deep learning takes an edge over other techniques as it deduces the features automatically and performs massively parallel computations. Multiple feature maps will help in accurate prediction. The objective of the proposed work is to develop a Computer Aided Deep Learning System identify and localize COVID-19 virus from other viruses and pneumonia. It helps to detect COVID-19 within a short period of time thereby improving the lifetime of the individuals. SIIM-FISABIO-RSNA benchmark datasets are used to examine the proposed system. Recall, Precision, Accuracy-rate, and F-Measure are the metrics used to prove the integrity of the system. © 2023 IEEE.

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning.

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

Deep Learning-Based Segmentation and Classification of COVID-19 Infection Severity Levels from CT Scans

Due to the emergence of coronavirus disease 2019 (COVID-19) around the globe, the detection and treatment of COVID-19 patients is an extremely essential process in healthcare systems. Among many imaging technologies, Computed Tomography (CT) analyses with deep learning frameworks offer better efficiency than the other imaging modalities. To segment the infected COVID-19 Region-Of-Interests (ROIs), an Enriched 2.5D U-Net (E2.5D U-Net)-based deep learner has been used. In contrast, it requires categorization of infection severity to identify the patients in the prior stage. Hence, in this article, feature extraction with a classification framework is proposed to learn deep features related to the infection disease severity. In this framework, each segment from the E2.5D U-Net is fed to a deep learner such as DenseNet201 to extract the deep features. These features are learned independently by different machine learning classifiers to categorize the infection severity levels. It aids physicians in diagnosing COVID-19 patients in advance. But it needs other infection-related features to enhance the efficiency. Therefore, a multi-modeling classification framework is proposed. This framework extracts the handcrafted features from CT scans and concatenates them with the deep features to get the unified feature vector. Moreover, these feature vectors are trained by using the multi-modeling classifier for predicting the infection severity levels with higher accuracy. At last, the testing outcomes exhibit that the multi-modal classifier establishes a higher efficiency than the standard classifier frameworks. © 2022 Lavoisier. All rights reserved.

A Systematic Review Literature on Computer-Aided Detection Methods for COVID-19 Detection in X-Ray and CT Image Modalities

The novel coronavirus was spreading all over the world and causes Severe Acute Respiratory Syndrome coronavirus2 (SARS-CoV2). Failing to identify this syndrome in the early stage of infection leads to death. Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) testing procedure is used to detect infection of SARS-CoV2, but the false-negative rate of the RT-PCR is up to 61% in the early stage of testing. To improve the detection accuracy, Lung Computed Tomography (L-CT) and chest radiograph (CXR) image modalities are used along with RT-PCR clinical procedure. This paper presented a comprehensive survey of the recently used methods and its techniques are used in the computer-aided analysis of L-CT images and CXR images for SARS-CoV2 detection. The survey result might help overcome the limitations of the existing computer-aided image analysis methods and identify research opportunities in lung CT and chest X-Ray image analysis for SARS-CoV2 detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. METHODS: In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model's performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. RESULTS: The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. CONCLUSIONS: This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases).

The ensemble deep learning model for novel COVID-19 on CT images.

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.