An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images

Challenges, opportunities, and advances related to COVID-19 classification based on deep learning

The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities: - computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches is highlighted a future research possibility.

Texture-less surface reconstruction using shape-based image augmentation

The development of intelligent Robot-Assisted Minimally Invasive Surgery demands geometric reconstruction from endoscopic images. However, images of human tissue surfaces are commonly texture-less. Obtaining the dense depth map of a texture-less scene is very difficult because traditional feature-based 3D reconstruction methods cannot detect enough features to build dense correspondences for depth computation. Given this problem, this study proposes a novel reconstruction method based on our shape-based image augmentation method. The main contribution of this method is the provision of a novel means to resolve the texture-less problem mainly on the input data level. In our method, we first calculate two shape gradient maps using Shape-From-Shading (SFS) method and we build Fast Point Feature Histogram (FPFH) 3D descriptor map according to the shape. Second, a series of augmented images can be computed by combining shape gradient maps, FPFH map, and the original image with different weights. Finally, we detect features on the new augmented images. Based on feature calculated sparse depth information and SFS calculated dense shape information, we further integrate a rectified dense depth map. Experiments show that our method can reconstruct texture-less surfaces with good accuracy.

Comprehensive Survey of Machine Learning Systems for COVID-19 Detection

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.

DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans

The recent disaster of COVID-19 has brought the whole world to the verge of devastation because of its highly transmissible nature. In this pandemic, radiographic imaging modalities, particularly, computed tomography (CT), have shown remarkable performance for the effective diagnosis of this virus. However, the diagnostic assessment of CT data is a human-dependent process that requires sufficient time by expert radiologists. Recent developments in artificial intelligence have substituted several personal diagnostic procedures with computer-aided diagnosis (CAD) methods that can make an effective diagnosis, even in real time. In response to COVID-19, various CAD methods have been developed in the literature, which can detect and localize infectious regions in chest CT images. However, most existing methods do not provide cross-data analysis, which is an essential measure for assessing the generality of a CAD method. A few studies have performed cross-data analysis in their methods. Nevertheless, these methods show limited results in real-world scenarios without addressing generality issues. Therefore, in this study, we attempt to address generality issues and propose a deep learning-based CAD solution for the diagnosis of COVID-19 lesions from chest CT images. We propose a dual multiscale dilated fusion network (DMDF-Net) for the robust segmentation of small lesions in a given CT image. The proposed network mainly utilizes the strength of multiscale deep features fusion inside the encoder and decoder modules in a mutually beneficial manner to achieve superior segmentation performance. Additional pre- and post-processing steps are introduced in the proposed method to address the generality issues and further improve the diagnostic performance. Mainly, the concept of post-region of interest (ROI) fusion is introduced in the post-processing step, which reduces the number of false-positives and provides a way to accurately quantify the infected area of lung. Consequently, the proposed framework outperforms various state-of-the-art methods by accomplishing superior infection segmentation results with an average Dice similarity coefficient of 75.7%, Intersection over Union of 67.22%, Average Precision of 69.92%, Sensitivity of 72.78%, Specificity of 99.79%, Enhance-Alignment Measure of 91.11%, and Mean Absolute Error of 0.026.

A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases

With the outbreak of COVID-19 and the increasing number of infections worldwide, there has been a noticeable deficiency in healthcare provided by medical professionals. To cope with this situation, computational methods can be used in different steps of COVID-19 handling. The first step is to accurately and rapidly diagnose infected persons, because the time taken for the diagnosis is among the crucial factors to save human lives. This paper proposes a computationally fast network for the diagnosis of COVID-19 and pulmonary diseases, which can be used in telemedicine. The proposed network is called DLNet because it jointly encodes local binary patterns along with filter outputs of discrete cosine transform (DCT). The first layer in DLNet is the convolution layer in which the input image is convolved using DCT filters. Then, to avoid over-fitting, a binary hashing procedure is performed by fusing responses of different filters into a unique feature map. This map is used to generate block-wise histograms by binding local binary codes of the input image and the map values. We normalize these histograms to improve the robustness of the network against illumination changes. Experiments conducted on a public dataset demonstrate the rapidity and effectiveness of DLNet, where an average accuracy, sensitivity, and specificity of 98.86%, 98.06, and 99.24% have been achieved, respectively. Moreover, the proposed network has shown high tolerance to the missing parts in the medical image, which makes it suitable for the telemedicine scenario.

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections

Coronavirus disease (COVID-19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID-19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT-PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time-consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre-trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state-of-the-art segmentation methods was also performed. The experiments were carried out using a fivefold cross-validation technique on a publicly available database containing 100 single-slice CT scans from >40 patients with COVID-19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross-validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.

Head CT Image Segmentation and Three-Dimensional Reconstruction Technology Based on Human Anatomy

With the continuous development of computer science and technology, the level of medical image processing and analysis technology has been significantly improved. In order to further optimize the medical imaging technology and provide assistance for medical diagnosis and treatment, this study will explore the head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy, using two morphological operation methods of image expansion and image corrosion, as well as the triangulation method based on surface contour, Optimize CT image segmentation technology and three-dimensional reconstruction technology. The results show that the CT image segmentation technology based on human anatomy can obtain the more essential morphology and features of the target image, and significantly improve the image quality. The size of the threshold can have a certain impact on the 3D reconstruction effect and reconstruction time to a certain extent. The larger the threshold, the shorter the reconstruction time, but the worse the 3D reconstruction effect. This shows that the target image after fitting has a good reconstruction effect, but the threshold level should be kept at a low level. The head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy have good application effects and can be popularized and applied in clinical diagnosis and treatment.

A lightweight capsule network architecture for detection of COVID-19 from lung CT scans

COVID-19, a novel coronavirus, has spread quickly and produced a worldwide respiratory ailment outbreak. There is a need for large-scale screening to prevent the spreading of the disease. When compared with the reverse transcription polymerase chain reaction (RT-PCR) test, computed tomography (CT) is far more consistent, concrete, and precise in detecting COVID-19 patients through clinical diagnosis. An architecture based on deep learning has been proposed by integrating a capsule network with different variants of convolution neural networks. DenseNet, ResNet, VGGNet, and MobileNet are utilized with CapsNet to detect COVID-19 cases using lung computed tomography scans. It has found that all the four models are providing adequate accuracy, among which the VGGCapsNet, DenseCapsNet, and MobileCapsNet models have gained the highest accuracy of 99%. An Android-based app can be deployed using MobileCapsNet model to detect COVID-19 as it is a lightweight model and best suited for handheld devices like a mobile.

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks

This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.

Domain-Adaptive Artificial Intelligence-Based Model for Personalized Diagnosis of Trivial Lesions Related to COVID-19 in Chest Computed Tomography Scans

BACKGROUND

Early and accurate detection of COVID-19-related findings (such as well-aerated regions, ground-glass opacity, crazy paving and linear opacities, and consolidation in lung computed tomography (CT) scan) is crucial for preventive measures and treatment. However, the visual assessment of lung CT scans is a time-consuming process particularly in case of trivial lesions and requires medical specialists.

METHOD

A recent breakthrough in deep learning methods has boosted the diagnostic capability of computer-aided diagnosis (CAD) systems and further aided health professionals in making effective diagnostic decisions. In this study, we propose a domain-adaptive CAD framework, namely the dilated aggregation-based lightweight network (DAL-Net), for effective recognition of trivial COVID-19 lesions in CT scans. Our network design achieves a fast execution speed (inference time is 43 ms on a single image) with optimal memory consumption (almost 9 MB). To evaluate the performances of the proposed and state-of-the-art models, we considered two publicly accessible datasets, namely COVID-19-CT-Seg (comprising a total of 3520 images of 20 different patients) and MosMed (including a total of 2049 images of 50 different patients).

RESULTS

Our method exhibits average area under the curve (AUC) up to 98.84%, 98.47%, and 95.51% for COVID-19-CT-Seg, MosMed, and cross-dataset, respectively, and outperforms various state-of-the-art methods.

CONCLUSIONS

These results demonstrate that deep learning-based models are an effective tool for building a robust CAD solution based on CT data in response to present disaster of COVID-19.

Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data

The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals. As a result, the researchers and technocrats are continuously working across the world for the inculcation of efficient strategies which may assist the government and healthcare system in controlling and managing the spread of the COVID-19 pandemic. Therefore, this study provides an extensive review of the ongoing strategies such as diagnosis, prediction, drug and vaccine development and preventive measures used in combating the COVID-19 along with technologies used and limitations. Moreover, this review also provides a comparative analysis of the distinct type of data, emerging technologies, approaches used in diagnosis and prediction of COVID-19, statistics of contact tracing apps, vaccine production platforms used in the COVID-19 pandemic. Finally, the study highlights some challenges and pitfalls observed in the systematic review which may assist the researchers to develop more efficient strategies used in controlling and managing the spread of COVID-19.

The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis

Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90–0.91), specificity was 0.91 (95% CI, 0.90–0.92) and the AUC was 0.96 (95% CI, 0.91–0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.88 (95% CI, 0.87–0.88) and the AUC was 0.96 (95% CI, 0.93–0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.95 (95% CI, 0.94–0.95) and the AUC was 0.97 (95% CI, 0.96–0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.