Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning DOI Creative Commons
Qanita Bani Baker, Mahmoud Hammad, Mohammad AL-Smadi

et al.

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(10), P. 250 - 250

Published: Oct. 13, 2024

The global spread of Coronavirus (COVID-19) has prompted imperative research into scalable and effective detection methods to curb its outbreak. early diagnosis COVID-19 patients emerged as a pivotal strategy in mitigating the disease. Automated using Chest X-ray (CXR) imaging significant potential for facilitating large-scale screening epidemic control efforts. This paper introduces novel approach that employs state-of-the-art Convolutional Neural Network models (CNNs) accurate detection. employed datasets each comprised 15,000 images. We addressed both binary (Normal vs. Abnormal) multi-class (Normal, COVID-19, Pneumonia) classification tasks. Comprehensive evaluations were performed by utilizing six distinct CNN-based (Xception, Inception-V3, ResNet50, VGG19, DenseNet201, InceptionResNet-V2) As result, Xception model demonstrated exceptional performance, achieving 98.13% accuracy, 98.14% precision, 97.65% recall, 97.89% F1-score classification, while multi-classification it yielded 87.73% 90.20% an 87.49% F1-score. Moreover, other utilized models, such competitive performance compared with many recent works.

Language: Английский

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review DOI
Elias Hossain, Rajib Rana, Niall Higgins

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 155, P. 106649 - 106649

Published: Feb. 10, 2023

Language: Английский

Citations

170
RNON: image inpainting via repair network and optimization network DOI Open Access
Yuantao Chen,

Runlong Xia,

Ke Zou

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2023, Volume and Issue: 14(9), P. 2945 - 2961

Published: March 25, 2023

Language: Английский

Citations

59
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
Driver Behavior Classification: A Systematic Literature Review DOI Creative Commons
Soukaina Bouhsissin, Nawal Sael, Faouzia Benabbou

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 14128 - 14153

Published: Jan. 1, 2023

Driver behavior is receiving increasing attention as a result of the staggering number road accidents. Many safety reports regard human most important factor in likelihood The detection and classification aggressive or abnormal driver an essential requirement real world to avoid deadly accidents protect users. automatic driver's aids prevention dangerous situations for all other participants driving environment, well implementation corrective measures. This paper presents systematic literature review (SLR) behavior. study aim highlight analyze different types behavior, data sources, datasets, features, artificial intelligence techniques used classify its performance. Based on results obtained from analysis selected works, we identify key contributions challenges studying propose potential avenues further directions practitioners researchers.

Language: Английский

Citations

50
PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates DOI Open Access
Yogesh H. Bhosale, K. Sridhar Patnaik

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 81, P. 104445 - 104445

Published: Nov. 30, 2022

Language: Английский

Citations

65
Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey DOI Creative Commons

Mohammad Hasan Ahmadilivani,

Alberto Bosio, Bastien Deveautour

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 131788 - 131828

Published: Jan. 1, 2022

In the modern-day era of technology, a paradigm shift has been witnessed in areas involving applications Artificial Intelligence (AI), Machine Learning (ML), and Deep (DL). Specifically, Neural Networks (DNNs) have emerged as popular field interest most AI such computer vision, image video processing, robotics, etc. context developed digital technologies availability authentic data handling infrastructure, DNNs credible choice for solving more complex real-life problems. The performance accuracy DNN is way better than human intelligence certain situations. However, it noteworthy that computationally too cumbersome terms resources time to handle these computations. Furthermore, general-purpose architectures like CPUs issues intensive algorithms. Therefore, lot efforts invested by research fraternity specialized hardware Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Coarse Grained Reconfigurable (CGRA) effective implementation This paper brings forward various works on development deployment using aforementioned embedded accelerators. review discusses detailed description hardware-based accelerators used training and/or inference DNN. A comparative study based factors power, area, throughput, also made discussed. Finally, future directions, trends accelerators, are article intended guide architects accelerate improve effectiveness deep learning research.

Language: Английский

Citations

51
A Systematic Literature Review on Machine Learning and Deep Learning Methods for Semantic Segmentation DOI
Ali Sohail, Naeem A. Nawaz, Asghar Ali Shah

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 134557 - 134570

Published: Jan. 1, 2022

Machine learning and deep algorithms are widely used in computer science domains. These mostly for classification regression problems almost every field of life. Semantic segmentation is an instantly growing research topic the last few decades that refers to association each pixel image class it belongs. This paper illustrates systematic survey advanced semantic till date. study provides brief knowledge about latest proposed methods domain segmentation. The comprehends concepts, techniques, tool, results different frameworks context discusses papers which machine techniques exploited published between 2016 2021. literature review collected from seven article libraries including ACM digital Library, Google Scholar, IEEE Xplore, Science Direct, Books, Refseek Worldwide Science. For assuring quality those selected have several citations on standardized platforms. Most studies COCO, PASCAL, Cityscapes CamVid dataset training validation models. articles form accuracy, mIoU value, F1 score, precision, recall. In this study, we also conclude most use ResNet as backbone architecture none researchers ensemble loophole studies.

Language: Английский

Citations

39
PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer DOI Open Access

Tianmu Wang,

Zhenguo Nie, Ruijing Wang

et al.

Medical & Biological Engineering & Computing, Journal Year: 2023, Volume and Issue: 61(6), P. 1395 - 1408

Published: Jan. 31, 2023

Language: Английский

Citations

38
Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review DOI Open Access
Jun Zhang, Jingyue Wu,

Yiyi Qiu

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 153, P. 106517 - 106517

Published: Jan. 5, 2023

Language: Английский

Citations

33
Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review DOI Creative Commons
Vasileios Skaramagkas, Anastasia Pentari, Zinovia Kefalopoulou

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2023, Volume and Issue: 31, P. 2399 - 2423

Published: Jan. 1, 2023

Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 January 2023 on learning techniques used in prognosis evolution of symptoms characteristics disease based gait, upper limb movement, speech facial expression-related information as well fusion more than one aforementioned modalities. The search resulted selection 87 original research publications, which we summarized relevant regarding utilized development process, demographic information, primary outcomes, sensory equipment related information. Various algorithms frameworks attained state-of-the-art performance many PD-related tasks by outperforming conventional machine approaches, according to reviewed. In meanwhile, identify significant drawbacks existing research, including a lack data availability interpretability models. fast advancements rise accessible provide opportunity address these difficulties near future for broad application this technology clinical settings.

Language: Английский

Citations

32