Automatic classification of severity of COVID-19 patients using texture feature and random forest based on computed tomography images

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care

BACKGROUND

e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients.

OBJECTIVE

The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic.

METHODS

The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients.

RESULTS

Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model).

CONCLUSIONS

Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective

Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of a threshold-based image segmentation technique and random forest classifier for COVID-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify COVID-19 individuals into three asperity classes such as early, progressive, and advanced, with an accuracy of 95.5% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify coronavirus severity.

Automatic Diagnosis of Stage of COVID-19 Patients using an Ensemble of Transfer Learning with Convolutional Neural Networks Based on Computed Tomography Images

Background

Diagnosis of the stage of COVID-19 patients using the chest computed tomography (CT) can help the physician in making decisions on the length of time required for hospitalization and adequate selection of patient care. This diagnosis requires very expert radiologists who are not available everywhere and is also tedious and subjective. The aim of this study is to propose an advanced machine learning system to diagnose the stages of COVID-19 patients including normal, early, progressive, peak, and absorption stages based on lung CT images, using an automatic deep transfer learning ensemble.

Methods

Different strategies of deep transfer learning were used which were based on pretrained convolutional neural networks (CNNs). Pretrained CNNs were fine-tuned on the chest CT images, and then, the extracted features were classified by a softmax layer. Finally, we built an ensemble method based on majority voting of the best deep transfer learning outputs to further improve the recognition performance.

Results

The experimental results from 689 cases indicate that the ensemble of three deep transfer learning outputs based on EfficientNetB4, InceptionResV3, and NasNetlarge has the highest results in diagnosing the stage of COVID-19 with an accuracy of 91.66%.

Conclusion

The proposed method can be used for the classification of the stage of COVID-19 disease with good accuracy to help the physician in making decisions on patient care.

Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices

Worldwide COVID-19 is a highly infectious and rapidly spreading disease in almost all age groups. The Computed Tomography (CT) scans of lungs are found to be accurate for the timely diagnosis of COVID-19 infection. In the proposed work, a deep learning-based P-shot N-ways Siamese network along with prototypical nearest neighbor classifiers is implemented for the classification of COVID-19 infection from lung CT scan slices. For this, a Siamese network with an identical sub-network (weight sharing) is used for image classification with a limited dataset for each class. The feature vectors are obtained from the pre-trained sub-networks having weight sharing. The performance of the proposed methodology is evaluated on the benchmark MosMed dataset having categories zero (healthy control) and numerous COVID-19 infections. The proposed methodology is evaluated on (a) chest CT scans provided by medical hospitals in Moscow, Russia for 1110 patients, and (b) case study of low-dose CT scans of 42 patients provided by Avtaran healthcare in India. The deep learning-based Siamese network (15-shot 5-ways) obtained an accuracy of 98.07%, the sensitivity of 95.66%, specificity of 98.83%, and F1-score of 95.10%. The proposed work outperforms the COVID-19 infection severity classification with limited scans availability for numerous infection categories.

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT.

Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists.

Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy.

Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.