Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation
Our approach balances high diagnostic accuracy with enhanced interpretability, addressing a critical barrier in applying DL models in clinical settings. Integrating of ANFIS and radiomics supports transparent decision-making, facilitating greater trust and applicability in real-world medical diagnostics assistance.
Authors
- Leondry Mayeta-Revilla (ORCID: https://orcid.org/0009-0007-7511-2305)
- Diego Mellado (ORCID: https://orcid.org/0000-0001-8078-253X)
- Julio A Sotelo (ORCID: https://orcid.org/0000-0002-0915-5215)
- Eduardo Cavieres (ORCID: https://orcid.org/0009-0003-4343-4001)
- Matías Salinas
- Stéren Chabert (ORCID: https://orcid.org/0000-0002-2890-5077)
- Francisco Miguel Robles Moyano
- Marvin Querales (ORCID: https://orcid.org/0000-0003-2372-6050)
- Rodrigo Salas (ORCID: https://orcid.org/0000-0002-0350-6811)
- Sebastian Ponce
Institutions
- Hospital Carlos Van Buren (CL)
- Millennium Institute for Integrative Biology (CL)
- Federico Santa María Technical University (CL)
- University of Valparaíso (CL)
Publication Details
- Journal
- Frontiers in Neuroinformatics
- Published
- 2025-04-17
- DOI
- https://doi.org/10.3389/fninf.2025.1550432
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Institut Català de Nanociència i Nanotecnologia
- Agenția Națională pentru Cercetare și Dezvoltare
- Agencia Nacional de Investigación y Desarrollo
- CHIST-ERA