Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques

Varalakshmi, P.; Narayanan, Vasumathi; Rajasekar, Sakthi Jaya Sundar

doi:10.1155/2021/5522729

Cited by 18 publications

(12 citation statements)

References 19 publications

(27 reference statements)

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“…The framework has a strong classification ability and is performed rapidly on a medium-speed computer without GPU acceleration. Perumal et al [ 57 ] classified COVID-19 CT scans into CAP and normal CT images. For improved data analysis, the suggested system was compared against a variety of machine learning and deep learning classifiers.…”

Section: Classification-based Methodsmentioning

confidence: 99%

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

Hassan

Ren

Zhao

et al. 2022

Computers in Biology and Medicine

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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.

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Section: Classification-based Methodsmentioning

confidence: 99%

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

Hassan

Ren

Zhao

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…Shah et al [13] employed VGG-19 model and attained an accuracy of 94.52%. Perumal et al [14] suggested AlexNet paired with SVM model to identify COVID-19 using chest CT scan pictures with an accuracy of 96.69%. Further, a few research 46] employed multiple transfer learning models to identify COVID-19 patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

2022

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One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders.

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“…In conclusion, the preliminary study has demonstrated that the CT images in the COVID-19 diagnosis system have high specificity [19] with sufficient generalizability and in addition to this, the AI-based diagnosis model could able to obtain the optimum results with a maximum of 99.00%+0.09 accuracy. Moreover, the study in [24] has stated that CT scans are spotting the hazy gray areas in the lungs which are the primary sign of COVID-19, and found that CT Screening has high sensitivity in diagnosing COVID-19. As a result, CT scans are deliberated as a primary resource for monitoring and evaluating the COVID-19 patients with the acute respiratory syndrome.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, CT scans are deliberated as a primary resource for monitoring and evaluating the COVID-19 patients with the acute respiratory syndrome. There is a need for analyzing multiple images to monitor the progression of the disease for a patient manually which complicates the earlier detection [24] and failed to attain greater precision. Therefore, an Automated AI-based diagnosis system has come into practice to speed up the screening of many images with a high precision rate and also able to detect the patients even in asymptotic conditions.…”

Section: Related Workmentioning

confidence: 99%

“…The following study has assessed the efficacy of various machine learning and deep learning algorithms applied over the radiological images to resolve the clinical diagnosis issues. According to the statement of the authors in [24], the prime abnormalities of COVID-19 infected patients are Ground-Glass Opacities (GGO), consolidations, and nodules. These features are easily identifiable through deep learning mechanisms.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach

2022

KSII TIIS

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Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID + and COVID -. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

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Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques

Cited by 18 publications

References 19 publications

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

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

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach

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