Scholarlink

Multi-level brain tumor identification stands crucial for medical image diagnostics as it enabled clinicians to deliver exact diagnosis and treatment development. The research presents CerebriNet as an explainable deep learning framework which executes multiclass brain tumor classification by analyzing MRI scans. CerebriNet demonstrates exceptional accuracy of 99.24% in classifying four categories: glioma, meningioma, pituitary tumors, and non-tumorous cases. The interpretability abilities represent a main advantage of CerebriNet. Internal brain regions where tumors exist that shape predictions can be shown through the combination of Grad-CAM and Grad-CAM++ visualization functions in the model. The visual insights help professionals understand clinical decisions which leads to strong hospital staff trust as well as medical facility feasibility. CerebriNet provides trustworthy diagnostic support for healthcare professionals through its reliable tumor detection system that offers minimal misdiagnosis possibilities along with explainable features.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancing Brain Tumor Classification Using the Explainable CerebriNet Model

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • H. N. Lakshmi,
  • B. Rupa Devi,
  • T. Harikala,
  • G. Ramakrishna,
  • Penubaka Balaji,
  • Malla Sudhakara

摘要

Multi-level brain tumor identification stands crucial for medical image diagnostics as it enabled clinicians to deliver exact diagnosis and treatment development. The research presents CerebriNet as an explainable deep learning framework which executes multiclass brain tumor classification by analyzing MRI scans. CerebriNet demonstrates exceptional accuracy of 99.24% in classifying four categories: glioma, meningioma, pituitary tumors, and non-tumorous cases. The interpretability abilities represent a main advantage of CerebriNet. Internal brain regions where tumors exist that shape predictions can be shown through the combination of Grad-CAM and Grad-CAM++ visualization functions in the model. The visual insights help professionals understand clinical decisions which leads to strong hospital staff trust as well as medical facility feasibility. CerebriNet provides trustworthy diagnostic support for healthcare professionals through its reliable tumor detection system that offers minimal misdiagnosis possibilities along with explainable features.