Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives DOI Creative Commons
Showmick Guha Paul, Arpa Saha, Al Amin Biswas

et al.

Array, Journal Year: 2022, Volume and Issue: 17, P. 100271 - 100271

Published: Dec. 10, 2022

COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to during the last two years. Due benefits Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, CT identification, it been further researched area medical science period COVID-19. This study assessed performance investigated different machine learning (ML), deep (DL), combinations various ML, DL, AI approaches employed recent studies with diverse data formats combat problems arisen COVID-19 pandemic. Finally, this shows comparison among stand-alone ML DL-based research works regarding issues AI-based works. After in-depth analysis comparison, responds proposed questions presents future directions context. review work will guide groups develop viable applications based on models, also healthcare institutes, researchers, governments by showing them how these techniques can ease process tackling

Language: Английский

A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion DOI
A. S. Albahri, Ali M. Duhaim, Mohammed A. Fadhel

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 96, P. 156 - 191

Published: March 15, 2023

Language: Английский

Citations

352
Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images DOI
R. Rajagopal, R. Karthick,

P. Meenalochini

et al.

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 79, P. 104197 - 104197

Published: Sept. 22, 2022

Language: Английский

Citations

109
Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review DOI Creative Commons
Sunday Adeola Ajagbe, Matthew O. Adigun

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(2), P. 5893 - 5927

Published: May 29, 2023

Abstract Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps timely detection of any infectious disease (IDs) essential to management diseases prediction future occurrences. Many scientists scholars have implemented DL techniques for pandemics, IDs other healthcare-related purposes, these outcomes are with various limitations research gaps. For purpose achieving an accurate, efficient less complicated DL-based system therefore, this study carried out systematic literature review (SLR) on pandemics using techniques. The survey anchored by four objectives state-of-the-art forty-five papers seven hundred ninety retrieved from different scholarly databases was analyze evaluate trend application areas pandemics. This used tables graphs extracted related articles online repositories analysis showed that good tool pandemic prediction. Scopus Web Science given attention current because they contain suitable scientific findings subject area. Finally, presents forty-four (44) studies technique performances. challenges identified include low performance model due computational complexities, improper labeling absence high-quality dataset among others. suggests possible solutions such as development improved or reduction output layer architecture pandemic-prone considerations.

Language: Английский

Citations

68
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

55
A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks DOI Creative Commons
Mohaimenul Azam Khan Raiaan, Sadman Sakib, Nur Mohammad Fahad

et al.

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100470 - 100470

Published: April 24, 2024

Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming researchers, therefore we need efficient optimization techniques. In this systematic review, explore range of well used algorithms, including metaheuristic, statistical, sequential, numerical approaches, to fine-tune hyperparameters. Our offers an exhaustive categorization (HPO) algorithms investigates the fundamental concepts CNN, explaining role variants. Furthermore, literature review HPO employing above mentioned undertaken. A comparative analysis conducted based strategies, error evaluation accuracy results across various datasets assess efficacy methods. addition addressing current challenges HPO, our illuminates unresolved issues field. By providing insightful evaluations merits demerits objective assist researchers determining suitable method particular problem dataset. highlighting future directions synthesizing diversified knowledge, survey contributes significantly ongoing development optimization.

Language: Английский

Citations

47
Review of the metaheuristic algorithms in applications: Visual analysis based on bibliometrics (1994–2023) DOI
Guanghui Li,

Taihua Zhang,

Chieh-Yuan Tsai

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124857 - 124857

Published: July 23, 2024

Language: Английский

Citations

19
A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges DOI Creative Commons
Abdullah A. Abdullah, Masoud Muhammed Hassan, Yaseen T. Mustafa

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 36538 - 36562

Published: Jan. 1, 2022

In the last decade, Deep Learning (DL) has revolutionized use of artificial intelligence, and it been deployed in different fields healthcare applications such as image processing, natural language signal processing. DL models have also intensely used tasks disease diagnostics treatments. learning techniques surpassed other machine algorithms proved to be ultimate tools for many state-of-the-art applications. Despite all that success, classical deep limitations their tend very confident about predicted decisions because does not know when makes mistake. For field, this limitation can a negative impact on predictions since almost regarding patients diseases are sensitive. Therefore, Bayesian (BDL) developed overcome these limitations. Unlike DL, BDL uses probability distributions model parameters, which possible estimate whole uncertainties associated with outputs. regard, offers rigorous framework quantify sources model. This study reviews popular using benefits It reviewed recent architecture Convolutional Neural Networks Recurrent Networks. particular, discussed its medical imaging tasks, clinical electronic health records. Furthermore, paper covered deployment some widespread diseases. fundamental research challenges highlighted gaps both perspective.

Language: Английский

Citations

67
Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network DOI Open Access
Gaffari Çelik

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 133, P. 109906 - 109906

Published: Dec. 7, 2022

Language: Английский

Citations

59
Multi‐texture features and optimized DeepNet for COVID‐19 detection using chest x‐ray images DOI
Anandbabu Gopatoti,

P. Vijayalakshmi

Concurrency and Computation Practice and Experience, Journal Year: 2022, Volume and Issue: 34(22)

Published: Aug. 1, 2022

Summary The corona virus disease 2019 (COVID‐19) pandemic has a severe influence on population health all over the world. Various methods are developed for detecting COVID‐19, but process of diagnosing this problem from radiology and radiography images is one effective procedures affected patients. Therefore, robust multi‐local texture features (MLTF)‐based feature extraction approach Improved Weed Sea‐based DeepNet (IWS‐based DeepNet) proposed COVID‐19 at an earlier stage. IWS‐based COVID‐19to optimize structure Deep Convolutional Neural Network (Deep CNN). IWS devised by incorporating Invasive Optimization (IIWO) Sea Lion (SLnO), respectively. noises present in input chest x‐ray (CXR) image discarded using Region Interest (RoI) adaptive thresholding technique. For extraction, MLFT newly considering various extracting best features. Finally, detection performed DeepNet. Furthermore, technique achieved performance terms True Positive Rate (TPR), Negative (TNR), accuracy with maximum values 0.933%, 0.890%, 0.919%.

Language: Английский

Citations

49
BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images DOI Creative Commons
Ghada Atteia, Amel Ali Alhussan, Nagwan Abdel Samee

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(15), P. 5520 - 5520

Published: July 24, 2022

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment ALL strongly associated with early diagnosis disease. Current practice for initial performed through manual evaluation stained smear microscopy images, which time-consuming and error-prone process. Deep learning-based human-centric biomedical has recently emerged as powerful tool assisting physicians making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify images. In this study, new Bayesian-based optimized convolutional neural network (CNN) introduced detection microscopic To promote classification performance, architecture proposed CNN its hyperparameters are customized input data Bayesian optimization approach. The technique adopts an informed iterative procedure search hyperparameter space optimal set that minimizes objective error function. trained validated using hybrid dataset formed integrating two public datasets. Data augmentation adopted further supplement image boost performance. search-derived model recorded improved performance image-based on test set. findings study reveal superiority Bayesian-optimized over other deep learning models.

Language: Английский

Citations

43