BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers.

Journal: Frontiers in oncology
Published Date: May 20, 2025

Abstract

BACKGROUND AND OBJECTIVE: Accurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians' subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time consumption, fatigue, and inconsistent diagnoses. Recently, deep learning technologies, particularly Convolutional Neural Networks (CNN), have achieved breakthrough advances in medical image analysis, offering a new paradigm for automated precise diagnosis. However, existing research largely focuses on single-task modeling, lacking comprehensive solutions that integrate tumor segmentation with classification diagnosis. This study aims to develop a multi-task deep learning model for precise brain tumor segmentation and type classification.

Authors

  • Cheng Lv
  • Xu-Jun Shu
    Medical school of Chinese PLA, Beijing 100853, China; Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing 100853, China.
  • Quan Liang
    Department of Radiology, Jinling Hospital, Nanjing, China.
  • Jun Qiu
    The School of Pediatrics, Hengyang Medical School, University of South China, Hunan Children's Hospital, Hengyang, Hunan, China.
  • Zi-Cheng Xiong
    Department of Computer and Information Engineering, Henan University, Nanchang, China.
  • Jing Bo Ye
    Department of Computer and Information Engineering, Henan University, Nanchang, China.
  • Shang Bo Li
    Department of Computer and Information Engineering, Henan University, Nanchang, China.
  • Cheng Qing Liu
    School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi, China.
  • Jing Zhen Niu
    Department of Critical Care Medicine, The Second People's Hospital of Yibin, Yibin, Sichuan, China.
  • Sheng-Bo Chen
    School of Computer and Information Engineering, Henan University, Henan Province 475004, China. Electronic address: 10120125@vip.henu.edu.cn.
  • Hong Rao
    School of Software, Nanchang University, Nanchang, Jiangxi 330031, China.

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