Applications of artificial intelligence in battling against covid-19: A literature review

Chaos Solitons & Fractals, Год журнала: 2020, Номер 142, С. 110338 - 110338

Опубликована: Окт. 3, 2020

Язык: Английский

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Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

Robotics and Autonomous Systems, Год журнала: 2021, Номер 146, С. 103902 - 103902

Опубликована: Окт. 7, 2021

Язык: Английский

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COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

International Journal of Environmental Research and Public Health, Год журнала: 2021, Номер 18(21), С. 11086 - 11086

Опубликована: Окт. 21, 2021

In the recent pandemic, accurate and rapid testing of patients remained a critical task in diagnosis control COVID-19 disease spread healthcare industry. Because sudden increase cases, most countries have faced scarcity low rate testing. Chest X-rays been shown literature to be potential source for patients, but manually checking X-ray reports is time-consuming error-prone. Considering these limitations advancements data science, we proposed Vision Transformer-based deep learning pipeline detection from chest X-ray-based imaging. Due lack large sets, collected three open-source sets images aggregated them form 30 K image set, which largest publicly available collection this domain our knowledge. Our transformer model effectively differentiates normal with an accuracy 98% along AUC score 99% binary classification task. It distinguishes COVID-19, normal, pneumonia patient’s 92% Multi-class For evaluation on fine-tuned some widely used models literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, DenseNet-121, as baselines. outperformed terms all metrics. addition, Grad-CAM based visualization created makes approach interpretable by radiologists can monitor progression affected lungs, assisting healthcare.

Язык: Английский

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A comprehensive review of deep learning-based single image super-resolution

PeerJ Computer Science, Год журнала: 2021, Номер 7, С. e621 - e621

Опубликована: Июль 13, 2021

Image super-resolution (SR) is one of the vital image processing methods that improve resolution an in field computer vision. In last two decades, significant progress has been made super-resolution, especially by utilizing deep learning methods. This survey effort to provide a detailed recent single-image perspective while also informing about initial classical used for super-resolution. The classifies SR into four categories, i.e., methods, supervised learning-based unsupervised and domain-specific We introduce problem intuition quality metrics, available reference datasets, challenges. Deep approaches are evaluated using dataset. Some reviewed state-of-the-art include enhanced network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual (MSRN), meta dense (Meta-RDN), recurrent back-projection (RBPN), second-order attention (SAN), feedback (SRFBN) wavelet-based (WRAN). Finally, this concluded with future directions trends open problems be addressed researchers.

Язык: Английский

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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

Journal of Advanced Research, Год журнала: 2022, Номер 48, С. 191 - 211

Опубликована: Сен. 7, 2022

Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging most well-known screening approach used for detecting pneumonia in early stages. While chest-Xray images are mostly blurry with low illumination, strong feature extraction required promising identification performance. A new hybrid explainable deep learning framework proposed accurate disease using chest images. The workflow developed by fusing capabilities both ensemble convolutional networks and Transformer Encoder mechanism. backbone to extract features from raw input two different scenarios: (i.e., DenseNet201, VGG16, GoogleNet) B InceptionResNetV2, Xception). Whereas, built based on self-attention mechanism multilayer perceptron (MLP) identification. visual saliency maps derived emphasize crucial predicted regions end-to-end training process models over all scenarios performed binary multi-class classification scenarios. model recorded 99.21% performance terms overall accuracy F1-score task, while it achieved 98.19% 97.29% multi-classification task. For scenario, 97.22% 97.14% F1-score, 96.44% F1-score. multiclass 97.2% 95.8% 96.4% 94.9% could provide encouraging comparing individual, models, or even latest AI literature. code available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.

Язык: Английский

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InSiNet: a deep convolutional approach to skin cancer detection and segmentation

Medical & Biological Engineering & Computing, Год журнала: 2022, Номер 60(3), С. 643 - 662

Опубликована: Янв. 13, 2022

Язык: Английский

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VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm

Neural Computing and Applications, Год журнала: 2022, Номер 34(10), С. 8253 - 8274

Опубликована: Янв. 24, 2022

Язык: Английский

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Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions

International Journal of Production Research, Год журнала: 2023, Номер unknown, С. 1 - 23

Опубликована: Март 13, 2023

Recent years have witnessed increased pressure across the global healthcare system during COVID-19 pandemic. The pandemic shattered existing operations and taught us importance of a resilient sustainable system. Digitisation, specifically adoption Artificial Intelligence (AI) has positively contributed to developing in recent past. To understand how AI contributes building system, this study based on systematic literature review 89 articles extracted from Scopus Web Science databases is conducted. organised around several key themes such as applications, benefits, challenges using technology sector. It observed that wide applications radiology, surgery, medical, research, development Based analysis, research framework proposed an extended Antecedents, Practices, Outcomes (APO) framework. This comprises applications' antecedents, practices, outcomes for Consequently, three propositions are drawn study. Furthermore, our adopted theory, context methodology (TCM) provide future directions, which can be used reference point studies.

Язык: Английский

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PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction

Alexandria Engineering Journal, Год журнала: 2023, Номер 78, С. 406 - 418

Опубликована: Авг. 2, 2023

Agricultural productivity plays a vital role in global economic development and growth. When crops are affected by diseases, it adversely impacts nation's resources agricultural output. Early detection of crop diseases can minimize losses for farmers enhance production. In this study, we propose new hybrid deep learning model, PLDPNet, designed to automatically predict potato leaf diseases. The PLDPNet framework encompasses image collection, pre-processing, segmentation, feature extraction fusion, classification. We employ an ensemble approach combining features from two well-established models (VGG19 Inception-V3) generate more powerful features. leverages the concept vision transformers final prediction. To train evaluate utilize public dataset: early blight, late healthy leaves. Utilizing strength segmentation fusion feature, proposed achieves overall accuracy 98.66%, F1-score 96.33%. A comprehensive validation study is conducted using Apple (4 classes) tomato (10 datasets achieving impressive accuracies 96.42% 94.25%, respectively. These experimental findings confirm that provides effective accurate prediction making promising candidate practical applications.

Язык: Английский

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Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Sensors, Год журнала: 2023, Номер 23(1), С. 527 - 527

Опубликована: Янв. 3, 2023

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in real world domain. intelligence, driving force current technological revolution, been used many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, most importantly healthcare sector. With rise COVID-19 pandemic, several prediction detection methods using artificial have employed to understand, forecast, handle, curtail ensuing threats. In this study, recent related publications, methodologies medical reports were investigated purpose studying intelligence's role pandemic. This study presents comprehensive review specific attention machine learning, deep image processing, object detection, segmentation, few-shot learning studies that utilized tasks COVID-19. particular, genetic analysis, clinical data sound biomedical classification, socio-demographic anomaly health monitoring, personal protective equipment (PPE) observation, social control, patients' mortality risk approaches forecast threatening factors demonstrates artificial-intelligence-based algorithms integrated into Internet Things wearable devices quite effective efficient forecasting insights which actionable through wide usage. The results produced by prove is promising arena can be applied for disease prognosis, forecasting, drug discovery, development sector on global scale. We indeed played important helping fight against COVID-19, insightful knowledge provided here could extremely beneficial practitioners experts domain implement systems curbing next pandemic or disaster.

Язык: Английский

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