Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

---

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

arXiv (Cornell University), Journal Year: 2020, Volume and Issue: unknown

Published: Jan. 1, 2020

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around world by directly affecting lungs. COVID-19 medium-sized, coated virus with single-stranded RNA, and also one largest RNA genomes approximately 120 nm. The X-Ray computed tomography (CT) imaging modalities are widely used to obtain fast accurate medical diagnosis. Identifying from these images extremely challenging as it time-consuming prone human errors. Hence, artificial intelligence (AI) methodologies can be consistent high performance. Among AI methods, deep learning (DL) networks have gained popularity recently compared conventional machine (ML). Unlike ML, all stages feature extraction, selection, classification accomplished automatically in DL models. In this paper, complete survey studies on application techniques for diagnostic segmentation lungs discussed, concentrating works CT images. Additionally, review papers forecasting coronavirus prevalence different parts presented. Lastly, challenges faced detection using directions future research discussed.

Language: Английский

---

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

Journal of Imaging, Journal Year: 2022, Volume and Issue: 8(3), P. 65 - 65

Published: March 5, 2022

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During COVID-19 pandemic, ultrasound (LUS) or point-of-care (POCUS) been a popular diagnostic tool due to its unique capability logistical advantages over chest X-ray CT. Pneumonia/ARDS is associated sonographic appearances pleural line irregularities B-line artefacts, which are caused by interstitial thickening inflammation, increase number severity. Artificial intelligence (AI), particularly machine learning, increasingly used as critical that assists clinicians LUS image reading decision making. We conducted systematic review from academic databases (PubMed Google Scholar) preprints on arXiv TechRxiv state-of-the-art learning technologies for images diagnosis. Openly accessible datasets listed. Various architectures have employed evaluate showed high performance. This paper will summarize current development AI management outlook emerging trends combining AI-based robotics, telehealth, other techniques.

Language: Английский

---

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

BME Frontiers, Journal Year: 2022, Volume and Issue: 2022

Published: Jan. 1, 2022

The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, develop new techniques for diagnosis treatment. Although lung ultrasound imaging is a less established approach when compared other medical modalities such as X-ray CT, multiple studies have demonstrated its promise diagnose patients. At same time, many deep learning models been built improve diagnostic efficiency imaging. integration these initially parallel efforts led report applications in patients, most which demonstrate outstanding potential aid COVID-19. This invited review focused on provides comprehensive overview systems utilized data acquisition, associated datasets, models, comparative performance.

Language: Английский

---

FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable AI

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107407 - 107407

Published: Sept. 1, 2023

The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In scenarios like COVID-19, applicability of diagnostic modalities is crucial in medical diagnosis, where non-invasive ultrasound imaging has potential to be a useful biomarker. This research develops computer-assisted intelligent methodology for lung image classification by utilizing fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence particular decisions. fuzzy-pooling method finds better representative features classification. FPCNN model categorizes images into one three classes: covid, disease-free (normal), and pneumonia. Explanations decisions are ensure fairness an system. used Shapley Additive Explanation (SHAP) explain prediction models. black-box illustrated using SHAP explanation intermediate layers model. To determine most effective model, we have tested different state-of-the-art architectures various training strategies, including fine-tuned models, single-layer pooling at layers. Among architectures, Xception having achieves best results 97.2% accuracy. We hope our proposed will helpful clinical diagnosis covid-19 from (LUS) images.

Language: Английский

---

XCovNet: An optimized xception convolutional neural network for classification of COVID-19 from point-of-care lung ultrasound images

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(11), P. 33653 - 33674

Published: Sept. 23, 2023

Abstract Global livelihoods are impacted by the novel coronavirus (COVID-19) disease, which mostly affects respiratory system and spreads via airborne transmission. The disease has spread to almost every nation is still widespread worldwide. Early reliable diagnosis essential prevent development of this highly risky disease. computer-aided diagnostic model facilitates medical practitioners in obtaining a quick accurate diagnosis. To address these limitations, study develops an optimized Xception convolutional neural network, called "XCovNet," for recognizing COVID-19 from point-of-care ultrasound (POCUS) images. This employs stack modules, each slew feature extractors that enable it learn richer representations with fewer parameters. identifies presence classifying POCUS images containing Coronavirus samples, viral pneumonia healthy We compare evaluate proposed network state-of-the-art (SOTA) deep learning models such as VGG, DenseNet, Inception-V3, ResNet, Networks. By using XCovNet model, previous study's problems cautiously addressed overhauled achieving 99.76% accuracy, 99.89% specificity, 99.87% sensitivity, 99.75% F1-score. understand underlying behavior different tests performed on shuffle patterns. Thus, "XCovNet" can, regions where test kits limited, be used help radiologists detect patients through current situation.

Language: Английский

---

Video Classification of Cloth Simulations: Deep Learning and Position-Based Dynamics for Stiffness Prediction

Sensors, Journal Year: 2024, Volume and Issue: 24(2), P. 549 - 549

Published: Jan. 15, 2024

In virtual reality, augmented or animation, the goal is to represent movement of deformable objects in real world as similar possible world. Therefore, this paper proposed a method automatically extract cloth stiffness values from video scenes, and then they are applied material properties for simulation. We propose use deep learning (DL) models tackle issue. The Transformer model, combination with pre-trained architectures like DenseNet121, ResNet50, VGG16, VGG19, stands leading choice classification tasks. Position-Based Dynamics (PBD) computational framework widely used computer graphics physics-based simulations entities, notably cloth. It provides an inherently stable efficient way replicate complex dynamic behaviors, such folding, stretching, collision interactions. Our model characterizes based on softness-to-stiffness labels accurately categorizes videos using labeling. dataset utilized research derived meticulously designed stiffness-oriented experimental assessment encompasses extensive 3840 videos, contributing multi-label dataset. results demonstrate that our achieves impressive average accuracy 99.50%. These accuracies significantly outperform alternative RNN, GRU, LSTM, Transformer.

Language: Английский

---

Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Frontiers in Medicine, Journal Year: 2023, Volume and Issue: 10

Published: Nov. 3, 2023

In order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology precisely diagnose subset lung diseases using patient audio recordings. These include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis.Our proposed trains four deep learning algorithms on an input dataset consisting 920 files. files were recorded digital stethoscopes comprise Sound Database. The deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), bidirectional (CNN-BLSTM).The aforementioned evaluated metrics such as accuracy, precision, recall, F1-score. best performing algorithm, LSTM, has overall 98.82% F1-score 0.97.The algorithm's extremely high predictive can be attributed its penchant for capturing sequential patterns in time series based data. summary, this algorithm is able ingest recordings make precise disease predictions real-time.

Language: Английский

---

Comparative Analysis of Hybrid 1D-CNN-LSTM and VGG16-1D-LSTM for Lung Lesion Classification

Journal of Electrical Engineering and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Language: Английский

---

Automatic diagnosis of abdominal pathologies in untrimmed ultrasound videos

International Journal of Computer Assisted Radiology and Surgery, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

Language: Английский

---