ADAPTIVE FEATURE FUSION AND CONFIDENCE-AWARE ENSEMBLE LEARNING FOR AUTOMATED BREAST CANCER GRADING.
Manual evaluation of mitotic activity & nuclear atypia is laborious and prone to inter-observer variability; yet, accurate classification of breast cancer using histopathological pictures is essential for diagnosis and therapy planning. Automatic cancer of the breast grading using the MITOS-ATYPIA-1...
| Publicado en: | Scientific Culture Vol. 12; no. 5; pp. 296 - 308 |
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| Autores principales: | , |
| Formato: | Artículo |
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University of the Aegean
2026
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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=hlh&AN=193950279&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 193950279 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 5 pid: 47715 pub: University of the Aegean artinfo: ui: 193950279 10.5281/zenodo.1250028 ppf: 296 ppct: 12 formats: tig: atl: ADAPTIVE FEATURE FUSION AND CONFIDENCE-AWARE ENSEMBLE LEARNING FOR AUTOMATED BREAST CANCER GRADING. aug: au: Maheshwari, Neerudu Uma SatheesKumaran, S. affil: Department of Electronics and Communication Engineering, Anurag University, Hyderabad, India su: Tumor grading Data fusion (Statistics) Ensemble learning Classification algorithms Computer-assisted image analysis (Medicine) Image segmentation Cell cycle Cell nuclei sug: subj: Tumor grading Data fusion (Statistics) Ensemble learning Classification algorithms Computer-assisted image analysis (Medicine) Image segmentation Cell cycle Cell nuclei keyword: Attention mechanism Breast cancer grading Computer-aided diagnosis Deep learning Histopathological image analysis multi-task learning ab: Manual evaluation of mitotic activity & nuclear atypia is laborious and prone to inter-observer variability; yet, accurate classification of breast cancer using histopathological pictures is essential for diagnosis and therapy planning. Automatic cancer of the breast grading using the MITOS-ATYPIA-14 dataset is presented in this research utilising an ambiguously guided dual-branch hybrid architecture. Hybrid nuclei segmentation with U-Net and watersheds algorithms follows stain normalisation and contrast enhancement in the suggested approach. Mitotic or nuclear atypia features are extracted separately using a dual-branch feature learning technique that combines profound learning, structural morphology, and texture-based descriptors. The most discriminative characteristics are selected using an attention-based dynamic feature selection technique and then merged to create a complete representation. An ensemble model that incorporates Support Vector Machine, Random Forest, & Light GBM is used for classification in order to increase accuracy and resilience. By integrating mitotic & atypia scores using a modified grading method, the ultimate cancer grade is determined. The experimental findings show that the suggested framework outperforms the current methods, with a 97.82% accuracy and a 96.77% F1-score. Based on these outcomes, it seems like the suggested approach is a solid bet for automatic breast cancer grading. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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