Radiological Image and Text-Based Medical Concept Detection in Social Networks Using Hybrid Deep Learning.
Nowadays, the presence of health-related content on social networks is rapidly increasing. With the effect of these networks, a large number of medical images, diagnosed and interpreted by various experts, are shared online. Therefore, concept detection and image classification from medical images r...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 21 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
12/5/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190982948&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190982948 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 12/5/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 190982948 190982948 190982948 10.1007/s10916-025-02311-y 190982948 ppf: 1 ppct: 20 formats: tig: atl: Radiological Image and Text-Based Medical Concept Detection in Social Networks Using Hybrid Deep Learning. aug: au: Bayrakdar, Sumeyye Yucedag, Ibrahim affil: https://ror.org/04175wc52 Computer Engineering Department, Duzce University, Duzce, Turkey sug: subj: Social Networks Radiography Classification Health Information Automation Classification Algorithms Evaluation Convolutional Neural Networks Evaluation Neural Networks (Computer) Evaluation Unified Medical Language System Image Interpretation, Computer Assisted Data Mining Health Informatics Social Media Human Deep Learning Prediction Algorithms Precision Validation Studies Descriptive Statistics Concept Analysis X-Rays Radiography, Thoracic User-Computer Interface National Library of Medicine (U.S.) Radiography, Panoramic Software Sensitivity and Specificity Image Processing, Computer Assisted Information Science Knowledge Bases Semantics Predictive Validity ab: Nowadays, the presence of health-related content on social networks is rapidly increasing. With the effect of these networks, a large number of medical images, diagnosed and interpreted by various experts, are shared online. Therefore, concept detection and image classification from medical images remains a challenging task. In recent years, deep learning-based models have become increasingly popular for addressing these challenges. The primary objective of this study is to perform multi-label classification of radiological images shared on a social network by automatically assigning relevant medical concepts. These concepts are derived from the Unified Medical Language System (UMLS). In this study, Convolutional Neural Network (CNN) combined with feed forward neural networks and various image encoders, including VGG-19, DenseNet-121, ResNet-101, Xception, Efficient-B7, to predict the appropriate concepts. The proposed hybrid deep learning models were trained and evaluated using the ImageCLEF 2019 dataset. Further evaluation was performed using a custom dataset (Rdpd_Test_Ds) composed of radiological images and their associated comments collected from a social network. The performance of the models was assessed using precision, recall, and F1-score metrics. The evaluation results are promising, demonstrating high performance. To the best of our knowledge, this research is the first to apply deep learning-based models to radiological data collected from a social network, representing a novel and impactful contribution to the field. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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