AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.
The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This pap...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 245 - 262 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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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=184471487&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471487 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471487 184471487 184471487 10.1007/s10278-024-01199-3 184471487 ppf: 245 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection. aug: au: Biradar, Shivaprasad Virupakshappa affil: Department of Computer Science & Engineering, Sharnbasva University, Kalaburagi, Karnataka, India sug: subj: Brain Neoplasms Classification Brain Neoplasms Diagnosis Magnetic Resonance Imaging Radiographic Image Interpretation, Computer-Assisted Support Vector Machine Evaluation Human Reliability Validity Machine Learning Software Design Sensitivity and Specificity Reproducibility of Results ROC Curve ab: The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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