A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.
Brain tumor classification plays a vital role in the medical diagnosis of brain lesions, ensuring accurate detection and supporting effective treatment planning. In this study, an ensemble learning framework is introduced based on a class-wise model selection strategy that integrates ResNet101 and D...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 4; pp. 3183 - 3196 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2026
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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=196241828&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196241828 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: Aug2026 vid: 39 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196241828 189902876 196241828 196241828 10.1007/s10278-025-01762-6 196241828 ppf: 3183 ppct: 13 formats: tig: atl: A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification. aug: au: Alsamawi, Motea Al-Arashi, Waled Hussein Alkhawlani, Mohammed M. Almahri, Fatima Ali Amer Jid affil: https://ror.org/0520msa48 Department of Biomedical Engineering, University of Science and Technology, Sana'a, Yemen sug: subj: Brain Neoplasms Diagnosis Brain Neoplasms Classification Boosting Machine Learning Algorithms Classification Algorithms Detection Algorithms Deep Learning Prediction Models Sensitivity and Specificity Magnetic Resonance Imaging Diagnosis, Computer Assisted Image Processing, Computer Assisted Human Convolutional Neural Networks Descriptive Statistics Automation Validation Studies Comparative Studies Data Analysis Software Experimental Studies Glioma Meningioma Pituitary Neoplasms Precision Predictive Value of Tests Diagnostic Errors False Negative Results False Positive Results ab: Brain tumor classification plays a vital role in the medical diagnosis of brain lesions, ensuring accurate detection and supporting effective treatment planning. In this study, an ensemble learning framework is introduced based on a class-wise model selection strategy that integrates ResNet101 and DenseNet201 architectures to achieve enhanced classification accuracy. Both models were independently trained on a comprehensive brain MRI dataset containing 7041 images, using an 80/20 training–testing split. The ResNet101 model was trained on resized images, while the DenseNet201 model was trained on resized and CLAHE-preprocessed images. Additionally, the test dataset underwent image sharpening to improve structural details and boost classification performance. The proposed class-wise ensemble achieved an overall accuracy of 96.50%, precision of 96.43%, specificity of 98.84%, recall of 96.20%, and an F1 score of 96.25%, surpassing the performance of the individual models (ResNet101 and DenseNet201) across all evaluation metrics. These outcomes confirm the robustness and efficiency of the proposed framework, emphasizing the capability of class-wise ensemble methods to significantly improve brain tumor classification. This study contributes to the domain of medical image analysis by offering a reliable and highly precise approach for automated brain tumor diagnosis. 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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