Ensemble-based Convolutional Neural Networks for brain tumor classification in MRI: Enhancing accuracy and interpretability using explainable AI.

Journal: Computers in biology and medicine
Published Date: Jun 23, 2025

Abstract

BACKGROUND: Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagnosis and treatment planning. Magnetic resonance imaging (MRI) is a key diagnostic tool, and deep learning models have shown promise in automating tumor classification. However, challenges remain in achieving high accuracy while maintaining interpretability for clinical use.

Authors

  • Luis Sánchez-Moreno
    Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Av. Reina Mercedes s/n, Sevilla 41012, Spain.
  • A Perez-Peña
    Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Av. Reina Mercedes s/n, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain, Av. Reina Mercedes s/n, Sevilla 41012, Spain. Electronic address: aperez7@us.es.
  • L Duran-Lopez
    Robotics and Tech. of Computers Lab., Universidad de Sevilla, 41012, Seville, Spain; Escuela Técnica Superior de Ingeniería Informática (ETSII), Universidad de Sevilla, 41012, Seville, Spain; Escuela Politécnica Superior (EPS), Universidad de Sevilla, 41011, Seville, Spain; Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), Universidad de Sevilla, 41012, Seville, Spain. Electronic address: lduran@atc.us.es.
  • Juan P Dominguez-Morales

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