Scholarlink

Early identification of brain tumors is indeed pivotal in cancer diagnosis. Timely detection significantly enhances the prospects of successful treatment and improved survival rates for patients. Gliomas, originating from glial cells, represent a prevalent form of brain tumors in adults. Despite significant strides in medical image analysis and glioma research, achieving precise diagnoses remains a persistent challenge. Gliomas are broadly categorized into High Grade (HG) and Low Grade (LG), pivotal in assessing disease progression and devising treatment strategies. While Convolutional Neural Networks (CNNs) have shown impressive success in medical image classification, accurately classifying 3D medical images remains arduous. The inherent difficulty lies in optimizing CNNs for 3D volumetric classification. In our current study, we tackle this challenge by introducing a cascaded approach, combining CNNs with Long Short-Term Memory (LSTM) Networks, aimed at classifying 3D brain tumor MR images into HG and LG gliomas. Leveraging features from the pre-trained VGG-16 model, we fed these into the LSTM network to learn high-level feature representations for the classification of 3D brain tumor volumes. Our findings reveal that features extracted from VGG-16 outperformed those from AlexNet and ResNet, showcasing superior classification accuracy. This cascaded CNN-LSTM framework demonstrates promising potential in enhancing the precise classification of gliomas, marking a substantial step forward in accurate diagnostic methodologies for brain tumors.

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

CNN-LSTM Based Hybrid Approach for Precise Brain Tumour Classification

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Sitanath Biswas,
  • Shubhashree Sahoo,
  • Saswati Rakshit,
  • Sujata Dash

摘要

Early identification of brain tumors is indeed pivotal in cancer diagnosis. Timely detection significantly enhances the prospects of successful treatment and improved survival rates for patients. Gliomas, originating from glial cells, represent a prevalent form of brain tumors in adults. Despite significant strides in medical image analysis and glioma research, achieving precise diagnoses remains a persistent challenge. Gliomas are broadly categorized into High Grade (HG) and Low Grade (LG), pivotal in assessing disease progression and devising treatment strategies. While Convolutional Neural Networks (CNNs) have shown impressive success in medical image classification, accurately classifying 3D medical images remains arduous. The inherent difficulty lies in optimizing CNNs for 3D volumetric classification. In our current study, we tackle this challenge by introducing a cascaded approach, combining CNNs with Long Short-Term Memory (LSTM) Networks, aimed at classifying 3D brain tumor MR images into HG and LG gliomas. Leveraging features from the pre-trained VGG-16 model, we fed these into the LSTM network to learn high-level feature representations for the classification of 3D brain tumor volumes. Our findings reveal that features extracted from VGG-16 outperformed those from AlexNet and ResNet, showcasing superior classification accuracy. This cascaded CNN-LSTM framework demonstrates promising potential in enhancing the precise classification of gliomas, marking a substantial step forward in accurate diagnostic methodologies for brain tumors.