Scholarlink

Brain tumor classification plays an important role in the early diagnosis and treatment of both cancerous and noncancerous tumors. Traditional techniques for Magnetic resonance imaging (MRI) scan analysis require a lot of time and can sometimes cause errors. In recent years, deep learning models have been used to speed up the process with fewer chances of errors. The study involves a transfer learning technique employing pretrained models including ResNet101, ResNet50, VGG16. The models are then trained on a diverse, labeled datasets of MRI scans obtained from various open-source websites. To enhance the efficacy of a model, a Gabor filter is applied to ResNet-101. The results revealed that the accuracy achieved by ResNet-101 with Gabor filters is 95% while accuracy achieved by ResNet50 and VGG16 without Gabor filters are 90% and 94% respectively.

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

Deep Learning-Based Brain Tumor Classification: A Comparative Study of CNN Architectures

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Marium Mumtaz,
  • Umamah Bint Khalid,
  • Muddasar Naeem,
  • Syed Tahir Hussain Rizvi,
  • Musarat Abbas,
  • Antonio Coronato

摘要

Brain tumor classification plays an important role in the early diagnosis and treatment of both cancerous and noncancerous tumors. Traditional techniques for Magnetic resonance imaging (MRI) scan analysis require a lot of time and can sometimes cause errors. In recent years, deep learning models have been used to speed up the process with fewer chances of errors. The study involves a transfer learning technique employing pretrained models including ResNet101, ResNet50, VGG16. The models are then trained on a diverse, labeled datasets of MRI scans obtained from various open-source websites. To enhance the efficacy of a model, a Gabor filter is applied to ResNet-101. The results revealed that the accuracy achieved by ResNet-101 with Gabor filters is 95% while accuracy achieved by ResNet50 and VGG16 without Gabor filters are 90% and 94% respectively.