A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 139, P. 105014 - 105014

Published: Nov. 4, 2021

Language: Английский

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How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future

Reviews in Medical Virology, Journal Year: 2020, Volume and Issue: 31(5), P. 1 - 11

Published: Dec. 19, 2020

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) machine learning. AI-based model systems could improve pattern recognition of spread in populations predictions outbreaks different geographical locations. A variable minimal amount data available for signs symptoms Covid-19, allowing composite maximum likelihood algorithms be employed enhance accuracy diagnosis identify potential drugs. forecasting expected complement traditional approaches helping public health officials select better response preparedness measures against Covid-19 cases. helped address key issues but significant impact on global healthcare industry is yet achieved. capability AI challenges may make player operation future. Here, we present an overview prospective applications settings during ongoing

Language: Английский

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Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

Artificial Intelligence Review, Journal Year: 2021, Volume and Issue: 55(2), P. 1409 - 1439

Published: April 15, 2021

Since early 2020, the whole world has been facing deadly and highly contagious disease named coronavirus (COVID-19) World Health Organization declared pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have reported till late August Medical images such as chest X-rays Computed Tomography scans are becoming one main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting rapid decision-making saving lives. This article provides an extensive review AI-based methods to assist medical practitioners with comprehensive knowledge efficient for diagnosis. Nearly all so far along their pros cons well recommendations improvements discussed, including image acquisition, segmentation, classification, follow-up phases developed between 2019 AI machine learning technologies boosted accuracy Covid-19 diagnosis, most widely used deep implemented worked a small amount data presents detailed mythological analysis evaluation process detecting from images. However, due quick outbreak Covid-19, there not many ground-truth datasets available communities. It is necessary combine experts' observations information reliable paper suggests that future research may focus multi-modality models how select best model architecture where can introduce more intelligence systems capture characteristics diseases obtain results timely treatment .

Language: Английский

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Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 180, P. 115141 - 115141

Published: May 4, 2021

Language: Английский

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COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

Sensors, Journal Year: 2021, Volume and Issue: 21(21), P. 7286 - 7286

Published: Nov. 2, 2021

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), so on, that can be analyzed by artificial intelligence methods for early diagnosis diseases. Recently, the outbreak COVID-19 disease caused many deaths. Computer vision researchers support doctors employing deep learning techniques on images to diagnose patients. Various were proposed case classification. A new automated technique using parallel fusion optimization models. The starts with contrast enhancement combination top-hat Wiener filters. Two pre-trained models (AlexNet VGG16) are employed fine-tuned according target classes (COVID-19 healthy). Features extracted fused approach—parallel positive correlation. Optimal features selected entropy-controlled firefly method. classified machine classifiers multiclass vector (MC-SVM). Experiments carried out Radiopaedia database achieved an accuracy 98%. Moreover, detailed analysis conducted shows improved performance scheme.

Language: Английский

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A systematic review on AI/ML approaches against COVID-19 outbreak

Complex & Intelligent Systems, Journal Year: 2021, Volume and Issue: 7(5), P. 2655 - 2678

Published: July 5, 2021

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because their significant advantages. Although AI/ML applications provide satisfactory solutions to these can a wide diversity. This increase in number diversity confuse deciding which technique is suitable purposes. Because there no comprehensive review study, this study systematically analyzes summarizes related studies. research methodology been proposed conduct systematic literature framing questions, searching criteria relevant data extraction. Finally, 264 were taken into account after following inclusion exclusion criteria. be regarded as key element epidemic transmission prediction, diagnosis detection, drug/vaccine development. Six questions are explored with 50 approaches 8 patient outcome 14 techniques disease predictions, along five risk assessment COVID-19. It also covers method drug development, vaccines models datasets usage dataset AI/ML.

Language: Английский

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Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

Pathogens, Journal Year: 2021, Volume and Issue: 10(8), P. 1048 - 1048

Published: Aug. 18, 2021

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 deaths from COVID-19, making it worst pandemic since 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, agile containment strategies. In this review, we focus on potential Artificial Intelligence (AI) surveillance, diagnosis, outcome prediction, drug discovery vaccine development. With help big data, AI tries to mimic cognitive capabilities a human brain, such as problem-solving learning abilities. Machine Learning (ML), subset AI, holds special promise for solving problems based experiences gained curated data. Advances methods have created an unprecedented opportunity building surveillance systems using deluge real-time data generated within short span time. During pandemic, many reports discussed utility approaches prioritization, delivery, supply chain drugs, vaccines, non-pharmaceutical interventions. This review will discuss clinical AI-based models also limitations faced by systems, model generalizability, explainability, trust pillars real-life deployment healthcare.

Language: Английский

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Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 141, P. 105127 - 105127

Published: Dec. 11, 2021

Language: Английский

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Real-Time Implementation of AI-Based Face Mask Detection and Social Distancing Measuring System for COVID-19 Prevention

Scientific Programming, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 21

Published: Sept. 27, 2021

Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem China and even around world. This pandemic is having devastating effects on societies economies The increase number of COVID-19 tests gives more information about epidemic spread, which may lead to possibility surrounding prevent further infections. However, wearing face mask that prevents transmission droplets air maintaining an appropriate physical distance between people, reducing close contact with each other can still be beneficial combating this pandemic. Therefore, research paper focuses implementing Face Mask Social Distancing Detection model as embedded vision system. pretrained models such MobileNet, ResNet Classifier, VGG are used our context. People violating social distancing or not masks were detected. After deploying models, selected one achieved confidence score 100%. also provides comparative study different detection classification models. system performance evaluated terms precision, recall, F1-score, support, sensitivity, specificity, accuracy demonstrate practical applicability. performs F1-score 99%, sensitivity specificity Hence, solution tracks people without real-time scenario ensures by generating alarm if there violation scene places. existing camera infrastructure enable these analytics applied various verticals, well office building at airport terminals/gates.

Language: Английский

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PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Computational and Mathematical Methods in Medicine, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 18

Published: March 8, 2021

Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. entails five improvements: first n-conv stochastic pooling module. Second, neural network was proposed. Third, PatchShuffle introduced as regularization term. Fourth, an improved multiple-way data augmentation used. Fifth, Grad-CAM utilized to interpret our AI model. Results. The 10 runs with random seed on test set showed algorithm achieved microaveraged F1 score 95.79%. Moreover, method better than nine state-of-the-art approaches. Conclusion. This will help assist radiologists make more quickly accurately cases.

Language: Английский

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