Development of a deep learning‐based rapid visual screening method for seismic vulnerability assessment of existing RC buildings in Turkey DOI
Muhammet Ozdemir, Gaffari Çelik

Structural Concrete, Год журнала: 2025, Номер unknown

Опубликована: Июнь 2, 2025

Abstract Urbanization and population growth have increased the existing building stock, making it more challenging to assess seismic safety of buildings due time constraints, a lack skilled personnel, high economic costs. In this study, rapid visual screening method (RVS) was utilized prioritize vulnerability reinforced concrete (RC) buildings. Accordingly, an integrated model combining deep feature residual networks, learning‐based architecture relying on blocks, XGBoost proposed. Additionally, five most influential parameters for determining were identified using technique. RVS methods used collect data RC following earthquakes in Afyon, Bingöl, Van, Kahramanmaraş, resulting dataset 372 structures. The model's performance evaluated accuracy, precision, recall, F1‐score, specificity, AUC metrics. proposed achieved accuracy rate 94.66% Furthermore, only critical features, 82.66% obtained. Sensitivity analysis performed see effect model. addition, stability tested against parameter changes or possible erroneous inputs. results indicated that although sensitive changes, its predictions remained within certain limits showed stable behavior errors.

Язык: Английский

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

и другие.

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

Процитировано

58

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review DOI Creative Commons
Sunil Kumar,

Harish Kumar,

Gyanendra Kumar

и другие.

BMC Medical Imaging, Год журнала: 2024, Номер 24(1)

Опубликована: Фев. 1, 2024

Abstract Background Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in world. Medical research has identified pneumonia, lung cancer, Corona Virus Disease 2019 (COVID-19) as prominent diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission (PET) others, primarily employed medical assessments because they provide computed data that can be utilized input datasets for computer-assisted diagnostic systems. used to develop evaluate machine learning (ML) methods analyze predict diseases. Objective This review analyzes ML paradigms, modalities' utilization, recent developments Furthermore, also explores various available publically being Methods The well-known databases academic studies have been subjected peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, many more, were search relevant articles. Applied keywords combinations procedures with primary considerations such COVID-19, ML, convolutional neural networks (CNNs), transfer learning, ensemble learning. Results finding indicates X-ray preferred detecting while CT scan predominantly favored cancer. COVID-19 detection, datasets. analysis reveals X-rays scans surpassed all other techniques. It observed using CNNs yields a high degree accuracy practicability identifying Transfer complementary techniques facilitate analysis. is metric assessment.

Язык: Английский

Процитировано

24

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things DOI Creative Commons
Arslan Akram, Javed Rashid, M. Arfan Jaffar

и другие.

Skin Research and Technology, Год журнала: 2023, Номер 29(11)

Опубликована: Ноя. 1, 2023

Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve accuracy and efficiency analysis, CAD systems play a crucial role. segment classify lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques.

Язык: Английский

Процитировано

33

Computer aided diagnosis using Harris Hawks optimizer with deep learning for pneumonia detection on chest X-ray images DOI
V. Parthasarathy, S. Saravanan

International Journal of Information Technology, Год журнала: 2024, Номер 16(3), С. 1677 - 1683

Опубликована: Янв. 18, 2024

Язык: Английский

Процитировано

15

Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey DOI
Mohammed A. A. Al‐qaness,

Jie Zhu,

Dalal AL-Alimi

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(6), С. 3267 - 3301

Опубликована: Фев. 19, 2024

Язык: Английский

Процитировано

13

Fusion of transformer attention and CNN features for skin cancer detection DOI
Hatice Çatal Reis, Veysel Turk

Applied Soft Computing, Год журнала: 2024, Номер 164, С. 112013 - 112013

Опубликована: Июль 18, 2024

Язык: Английский

Процитировано

10

Medical Imaging-based Artificial Intelligence in Pneumonia: A Narrative Review DOI
Yanping Yang, Wenyu Xing, Yiwen Liu

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129731 - 129731

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels DOI Creative Commons
Zhanlin Ji, Jianuo Liu,

Juncheng Mu

и другие.

Medical & Biological Engineering & Computing, Год журнала: 2024, Номер 62(7), С. 2087 - 2100

Опубликована: Март 8, 2024

Abstract The pancreas not only is situated in a complex abdominal background but also surrounded by other organs and adipose tissue, resulting blurred organ boundaries. Accurate segmentation of pancreatic tissue crucial for computer-aided diagnosis systems, as it can be used surgical planning, navigation, assessment organs. In the light this, current paper proposes novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are to highlight features. Secondly, feature fusion between adjacent encoding layers fully utilizes low-level deep-level features extracted blocks. Finally, parallel dilated convolutions employed increase receptive field capture multiscale spatial information. ResDAC-Net highly compatible existing state-of-the-art models, according three (out four) evaluation metrics, including two main ones performance (i.e., DSC Jaccard index). Graphical abstract

Язык: Английский

Процитировано

7

CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals DOI Open Access
Gaffari Çelik

Computers in Biology and Medicine, Год журнала: 2023, Номер 163, С. 107153 - 107153

Опубликована: Июнь 8, 2023

Язык: Английский

Процитировано

14

Ensemble Federated Learning: An approach for collaborative pneumonia diagnosis DOI Creative Commons
Alhassan Mabrouk, Rebeca P. Dı́az Redondo, Mohamed Abd Elaziz

и другие.

Applied Soft Computing, Год журнала: 2023, Номер 144, С. 110500 - 110500

Опубликована: Июнь 19, 2023

Federated learning is a very convenient approach for scenarios where (i) the exchange of data implies privacy concerns and/or (ii) quick reaction needed. In smart healthcare systems, both aspects are usually required. this paper, we work on first scenario, preserving key and, consequently, building unique and massive medical image set by fusing different sets from institutions or research centers (computation nodes) not an option. We propose ensemble federated (EFL) that based following characteristics: First, each computation node works with (but same type). They locally apply combining eight well-known CNN models (densenet169, mobilenetv2, xception, inceptionv3, vgg16, resnet50, densenet121, resnet152v2) Chest X-ray images. Second, best two local used to create model shared central node. Third, aggregated obtain global model, which nodes continue new iteration. This procedure continues until there no changes in models. have performed experiments compare our centralized ones (with without approach)\color{black}. The results conclude proposal outperforms these images (achieving accuracy 96.63\%) offers competitive compared other proposals literature.

Язык: Английский

Процитировано

13