Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Sensors, Journal Year: 2021, Volume and Issue: 21(2), P. 455 - 455

Published: Jan. 11, 2021

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced network architectures proposed transfer strategy using custom-sized input tailored for each architecture to achieve the best performance. We conducted extensive sets of experiments two image datasets, namely, SARS-CoV-2 CT-scan COVID19-CT. The results show superior performances our compared with previous studies. Our achieved average accuracy, precision, sensitivity, specificity, F1-score values 99.4%, 99.6%, 99.8%, 99.4% dataset, 92.9%, 91.3%, 93.7%, 92.2%, 92.5% COVID19-CT respectively. For better interpretability results, applied visualization techniques provide visual explanations models’ predictions. Feature visualizations learned features well-separated clusters representing non-COVID-19 cases. Moreover, indicate that are not only capable identifying cases but also accurate localization COVID-19-associated regions, as indicated by well-trained radiologists.

Language: Английский

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DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

SN Computer Science, Journal Year: 2021, Volume and Issue: 2(5)

Published: July 23, 2021

Language: Английский

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Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review

Neural Processing Letters, Journal Year: 2022, Volume and Issue: 55(3), P. 3551 - 3603

Published: Sept. 16, 2022

Language: Английский

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Recent progress in transformer-based medical image analysis

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107268 - 107268

Published: July 20, 2023

Language: Английский

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A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

BMC Medical Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 1, 2024

Abstract Background Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in world. Medical research has identified pneumonia, lung cancer, Corona Virus Disease 2019 (COVID-19) as prominent diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission (PET) others, primarily employed medical assessments because they provide computed data that can be utilized input datasets for computer-assisted diagnostic systems. used to develop evaluate machine learning (ML) methods analyze predict diseases. Objective This review analyzes ML paradigms, modalities' utilization, recent developments Furthermore, also explores various available publically being Methods The well-known databases academic studies have been subjected peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, many more, were search relevant articles. Applied keywords combinations procedures with primary considerations such COVID-19, ML, convolutional neural networks (CNNs), transfer learning, ensemble learning. Results finding indicates X-ray preferred detecting while CT scan predominantly favored cancer. COVID-19 detection, datasets. analysis reveals X-rays scans surpassed all other techniques. It observed using CNNs yields a high degree accuracy practicability identifying Transfer complementary techniques facilitate analysis. is metric assessment.

Language: Английский

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Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

Medical & Biological Engineering & Computing, Journal Year: 2021, Volume and Issue: 59(4), P. 825 - 839

Published: March 18, 2021

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of crucial for containment it has caused. In this study, transfer learning-based screening technique proposed. motivation study design an automated system that can assist medical staff especially in areas where trained are outnumbered. investigates potential models automatically diagnosing diseases like force, times outbreak. proposed work, deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) implemented screen CT scans. architecture fine-tuned used extract features scan images. Further principal component analysis (PCA) feature selection. For final classification, four different classifiers, namely convolutional neural network (DCNN), extreme machine (ELM), online sequential ELM, bagging ensemble with support vector (SVM) compared. best performing classifier SVM within 385 ms achieved accuracy 95.7%, precision 95.8%, area under curve (AUC) 0.958, F1 score 95.3% on 208 test results obtained diverse datasets prove superiority robustness work. A pre-processing also been radiological data. further compares pre-trained CNN architectures classification against technique.

Language: Английский

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A bi-stage feature selection approach for COVID-19 prediction using chest CT images

Applied Intelligence, Journal Year: 2021, Volume and Issue: 51(12), P. 8985 - 9000

Published: April 19, 2021

Language: Английский

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Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: July 8, 2021

Abstract COVID-19 has crippled the world’s healthcare systems, setting back economy and taking lives of several people. Although potential vaccines are being tested supplied around world, it will take a long time to reach every human being, more so with new variants virus emerging, enforcing lockdown-like situation on parts world. Thus, there is dire need for early accurate detection prevent spread disease, even more. The current gold-standard RT-PCR test only 71% sensitive laborious perform, leading incapability conducting population-wide screening. To this end, in paper, we propose an automated system that uses CT-scan images lungs classifying same into COVID Non-COVID cases. proposed method applies ensemble strategy generates fuzzy ranks base classification models using Gompertz function fuses decision scores adaptively make final predictions Three transfer learning-based convolutional neural network used, namely VGG-11, Wide ResNet-50-2, Inception v3, generate be fused by model. framework been evaluated two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying reliability relevant source codes related present work in: GitHub.

Language: Английский

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FBSED based automatic diagnosis of COVID-19 using X-ray and CT images

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 134, P. 104454 - 104454

Published: May 2, 2021

Language: Английский

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Ear Recognition Based on Deep Unsupervised Active Learning

IEEE Sensors Journal, Journal Year: 2021, Volume and Issue: 21(18), P. 20704 - 20713

Published: July 26, 2021

Cooperative machine learning has many applications, such as data annotation, where an initial model trained with partially labeled is used to predict labels for unseen continuously. Predicted a low confidence value are manually revised allow the be retrained predicted and data. In this paper, we propose alternative approach: training process called Deep Unsupervised Active Learning. Using proposed scheme, classification can incrementally acquire new knowledge during testing phase without manual guidance or correction of decision making. The consists two stages: first stage supervised using model, unsupervised active test phase. phase, high confidence, continuously extend base model. To optimize method, must have recognition rate. end, exploited Visual Geometric Group (VGG16) pre-trained applied three datasets: Mathematical Image Analysis (AMI), University Science Technology Beijing (USTB2), Annotated Web Ears (AWE). This approach achieved impressive performance that shows significant improvement in rate USTB2 dataset by coloring its images Generative Adversarial Network (GAN). obtained performances interesting compared current methods: rates 100.00%, 98.33%, 51.25% USTB2, AMI, AWE datasets, respectively.

Language: Английский

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