ShipGAN: Generative Adversarial Network based simulation-to-real image translation for ships DOI Creative Commons
Yuxuan Dong, Peng Wu, Sen Wang

et al.

Applied Ocean Research, Journal Year: 2023, Volume and Issue: 131, P. 103456 - 103456

Published: Jan. 11, 2023

Recent advances in robotics and autonomous systems (RAS) have significantly improved the autonomy level of unmanned surface vehicles (USVs) made them capable undertaking demanding tasks various environments. During operation USVs, apart from normal situations, it is those unexpected scenes, such as busy waterways or navigation dust/nighttime, impose most dangers to USVs these scenes are rarely seen during training. Such a rare occurrence also makes manual collection recording into dataset difficult, expensive inefficient, with majority existing public available datasets not able fully cover them. One many plausible solutions purposely generate data using computer vision techniques assistance high-fidelity simulations that can create desirable motions/scenarios. However, stylistic difference between simulation images natural would cause domain shift problem. Hence, there need for designing method transfer distribution styles realistic domain. This paper proposes evaluates novel solution fill this gap Generative Adversarial Network (GAN) based model, ShipGAN, translate images. Experiments were carried out investigate feasibility generating GAN-based image translation models. The synthetic demonstrated be reliable by object detection segmentation algorithms trained

Language: Английский

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications DOI Creative Commons
Laith Alzubaidi, Jinshuai Bai, Aiman Al-Sabaawi

et al.

Journal Of Big Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: April 14, 2023

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate train frameworks. Usually, manual labeling needed provide labeled data, which typically involves human annotators with vast background knowledge. This annotation process costly, time-consuming, and error-prone. every framework fed by significant automatically learn representations. Ultimately, larger would generate better model its performance also application dependent. issue the main barrier for dismissing use DL. Having sufficient first step toward any successful trustworthy application. paper presents holistic survey on state-of-the-art techniques deal models overcome three challenges including small, imbalanced datasets, lack generalization. starts listing techniques. Next, types architectures are introduced. After that, solutions address listed, such as Transfer Learning (TL), Self-Supervised (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these were followed some related tips about acquisition prior purposes, well recommendations ensuring trustworthiness dataset. The ends list that suffer from scarcity, several alternatives proposed in order more each Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, Cybersecurity. To best authors’ knowledge, this review offers comprehensive overview strategies tackle

Language: Английский

Citations

354
A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations DOI Creative Commons
Zehui Zhao, Laith Alzubaidi, Jinglan Zhang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 242, P. 122807 - 122807

Published: Dec. 2, 2023

Deep learning has emerged as a powerful tool in various domains, revolutionising machine research. However, one persistent challenge is the scarcity of labelled training data, which hampers performance and generalisation deep models. To address this limitation, researchers have developed innovative methods to overcome data enhance model capabilities. Two prevalent techniques that gained significant attention are transfer self-supervised learning. Transfer leverages knowledge learned from pre-training on large-scale dataset, such ImageNet, applies it target task with limited data. This approach allows models benefit representations effectively new tasks, resulting improved generalisation. On other hand, focuses using pretext tasks do not require manual annotation, allowing them learn valuable large amounts unlabelled These can then be fine-tuned for downstream mitigating need extensive In recent years, found applications fields, including medical image processing, video recognition, natural language processing. approaches demonstrated remarkable achievements, enabling breakthroughs areas disease diagnosis, object understanding. while these offer numerous advantages, they also limitations. For example, may face domain mismatch issues between requires careful design ensure meaningful representations. review paper explores fields within past three years. It delves into advantages limitations each approach, assesses employing techniques, identifies potential directions future By providing comprehensive current methods, article offers guidance selecting best technique specific issue.

Language: Английский

Citations

109
RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection DOI Open Access
Yasin Kaya, Ercan Gürsoy

Soft Computing, Journal Year: 2023, Volume and Issue: 27(9), P. 5521 - 5535

Published: Jan. 4, 2023

Language: Английский

Citations

72
Deep learning models-based CT-scan image classification for automated screening of COVID-19 DOI
Kapil Gupta, Varun Bajaj

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 80, P. 104268 - 104268

Published: Sept. 30, 2022

Language: Английский

Citations

70
Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

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

Published: Jan. 26, 2024

Language: Английский

Citations

56
Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning DOI Open Access
Navid Ghassemi, Afshin Shoeibi, Marjane Khodatars

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 144, P. 110511 - 110511

Published: June 15, 2023

Language: Английский

Citations

49
Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs DOI Creative Commons
Ahmad MohdAziz Hussein, Abdulrauf Garba Sharifai, Osama Moh’d Alia

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 4, 2024

Abstract The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this has several drawbacks, including high cost, lengthy turnaround time results, and the potential false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed diagnosing disease. Chest are more commonly than CT scans widespread availability of X-ray machines, lower ionizing radiation, cost equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary radiologists manually search biomarkers. process time-consuming prone errors. Therefore, there a critical need develop an automated system evaluating X-rays. Deep learning techniques expedite process. In study, deep learning-based called Custom Convolutional Neural Network (Custom-CNN) proposed identifying infection in Custom-CNN model consists eight weighted layers utilizes strategies like dropout batch normalization enhance performance reduce overfitting. approach achieved classification accuracy 98.19% aims accurately classify COVID-19, normal, pneumonia samples.

Language: Английский

Citations

20
Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges DOI Creative Commons
Mahmoud K. Ibrahim, Yasmina Al Khalil, Sina Amirrajab

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109834 - 109834

Published: March 1, 2025

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, X-ray), text, time-series, tabular (EHR). Unlike previous narrowly focused reviews, our study encompasses broad array modalities explores models. Our aim is offer insights into their current future applications in research, particularly the context synthesis applications, generation techniques, evaluation methods, as well providing GitHub repository dynamic resource for ongoing collaboration innovation. search strategy queries databases such Scopus, PubMed, ArXiv, focusing on recent works from January 2021 November 2023, excluding reviews perspectives. period emphasizes advancements beyond GANs, which have been extensively covered reviews. The survey also aspect conditional generation, not similar work. Key contributions include broad, multi-modality scope that identifies cross-modality opportunities unavailable single-modality surveys. While core techniques are transferable, we find methods often lack sufficient integration patient-specific context, clinical knowledge, modality-specific requirements tailored unique characteristics data. Conditional leveraging textual conditioning multimodal remain underexplored but promising directions findings structured around three themes: (1) Synthesis highlighting clinically valid significant gaps using synthetic augmentation, validation evaluation; (2) Generation identifying personalization innovation; (3) Evaluation revealing absence standardized benchmarks, need large-scale validation, importance privacy-aware, relevant frameworks. These emphasize benchmarking comparative studies promote openness collaboration.

Language: Английский

Citations

5
Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals DOI
Şengül Doğan, Mehmet Bayğın, Burak Taşçı

et al.

Cognitive Neurodynamics, Journal Year: 2022, Volume and Issue: 17(3), P. 647 - 659

Published: Aug. 12, 2022

Language: Английский

Citations

60
Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss DOI Open Access

Ekram Chamseddine,

Nesrine Mansouri,

Makram Soui

et al.

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 129, P. 109588 - 109588

Published: Aug. 29, 2022

Language: Английский

Citations

54