Scholarlink

The potentially fatal nature of brain tumors makes diagnosis and treatment extremely difficult. Optimizing patient outcomes and achieving an early and accurate diagnosis are critical. The introduction of Computer-Aided Diagnosis (CAD) technologies has greatly improved traditional MRI procedures, improving the precision and efficacy of tumor identification and categorization. This review uses artificial intelligence and machine learning to analyze recent developments in brain tumor detection, classification, and segmentation. We examine important datasets that are essential for creating reliable CAD systems, such as those from Kaggle, the BraTS 2019, the IXI Dataset, and other web resources. Two well-known methods for detection and classification that show great potential in the detection of brain tumors are Convolutional Neural Networks (CNN) and Transfer Learning. We explore advanced segmentation techniques such as shape-based topological methods, Rough-Fuzzy C-means (RFCM), and the innovative Swin UNETR. These cutting-edge techniques significantly increase efficiency and accuracy, enabling clinicians to make better decisions. The potential to transform the management of brain tumors and enhance patient outcomes exists when these tools are included into clinical procedures.

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

Brain Tumor Diagnosis: Reviewing Modern Methods of Detection, Classification, and Segmentation

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Sourav Barman,
  • Tasniya Ahmed,
  • Sunaan Sultan,
  • Shaila Sharmin,
  • Shudeb Babu Sen Omit

摘要

The potentially fatal nature of brain tumors makes diagnosis and treatment extremely difficult. Optimizing patient outcomes and achieving an early and accurate diagnosis are critical. The introduction of Computer-Aided Diagnosis (CAD) technologies has greatly improved traditional MRI procedures, improving the precision and efficacy of tumor identification and categorization. This review uses artificial intelligence and machine learning to analyze recent developments in brain tumor detection, classification, and segmentation. We examine important datasets that are essential for creating reliable CAD systems, such as those from Kaggle, the BraTS 2019, the IXI Dataset, and other web resources. Two well-known methods for detection and classification that show great potential in the detection of brain tumors are Convolutional Neural Networks (CNN) and Transfer Learning. We explore advanced segmentation techniques such as shape-based topological methods, Rough-Fuzzy C-means (RFCM), and the innovative Swin UNETR. These cutting-edge techniques significantly increase efficiency and accuracy, enabling clinicians to make better decisions. The potential to transform the management of brain tumors and enhance patient outcomes exists when these tools are included into clinical procedures.