Scholarlink

<p>Gliomas are the most prevalent and aggressive primary brain tumors characterized by rapid progressions and infiltration. Timely and precise diagnosis is crucial for effective oncology treatment. Magnetic Resonance Imaging (MRI) facilitates noninvasive assessment of brain lesions. Manual brain tumor evaluation from MRI scans is labor-intensive, relies heavily on clinician experience, and is prone to errors. Consequently, automated diagnosis of brain tumors is essential for optimal clinical management and glioma surgical interventions. This study introduces an Integrated Deep Convolutional Neural Network (IDCNN)-based framework for segmenting and grading glioma tumors from multimodal MRI scans. The framework integrates two state-of-the-art CNN architectures. Based on 3D U-Net architecture, the first CNN performs tumor segmentation from multimodal MRI volumes. The segmentation network utilizes the encoder for feature extraction and dimensionality reduction, while the decoder reconstructs the output to the original input size. A thorough performance evaluation of CNN architectures based on pre-trained (ResNet50, EfficientNetB0, DensNet121, EfficientNetB2, and ResNet152V2) was conducted on a three-class dataset of glioma, meningioma, and pituitary tumors to determine the best model for tumor grading. EfficientNetB2 surpassed other models across all evaluation metrics, achieving 99.19% test accuracy, 99.17% precision, 98.94% sensitivity, 99.57% specificity, and 99.06% F1-score. The optimal EfficientNetB2 model was subsequently utilized to grade tumors identified by the segmentation network. The proposed framework exhibited exceptional performance in both segmentation and grading tasks, attaining dice coefficient scores (DSC) of 86.13, 86.75, and 92.41 in enhancing tumor, tumor core, and whole tumor, respectively, alongside 98.49% classification accuracy for high- and low-grade glioma. Experimental findings validate the superior capabilities of the proposed framework compared to the existing methods. These results highlight the potential of the proposed model to aid radiologists in achieving accurate and reliable diagnoses, improving patient outcomes, and supporting clinical decision-making.</p>

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

An integrated deep convolutional neural networks framework for the automatic segmentation and grading of glioma tumors using multimodal MRI scans

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月13日

查看全文
  • Otung John Peter Odong,
  • Mohammed Abo-Zahhad,
  • Moataz Abdelwahab

摘要

Gliomas are the most prevalent and aggressive primary brain tumors characterized by rapid progressions and infiltration. Timely and precise diagnosis is crucial for effective oncology treatment. Magnetic Resonance Imaging (MRI) facilitates noninvasive assessment of brain lesions. Manual brain tumor evaluation from MRI scans is labor-intensive, relies heavily on clinician experience, and is prone to errors. Consequently, automated diagnosis of brain tumors is essential for optimal clinical management and glioma surgical interventions. This study introduces an Integrated Deep Convolutional Neural Network (IDCNN)-based framework for segmenting and grading glioma tumors from multimodal MRI scans. The framework integrates two state-of-the-art CNN architectures. Based on 3D U-Net architecture, the first CNN performs tumor segmentation from multimodal MRI volumes. The segmentation network utilizes the encoder for feature extraction and dimensionality reduction, while the decoder reconstructs the output to the original input size. A thorough performance evaluation of CNN architectures based on pre-trained (ResNet50, EfficientNetB0, DensNet121, EfficientNetB2, and ResNet152V2) was conducted on a three-class dataset of glioma, meningioma, and pituitary tumors to determine the best model for tumor grading. EfficientNetB2 surpassed other models across all evaluation metrics, achieving 99.19% test accuracy, 99.17% precision, 98.94% sensitivity, 99.57% specificity, and 99.06% F1-score. The optimal EfficientNetB2 model was subsequently utilized to grade tumors identified by the segmentation network. The proposed framework exhibited exceptional performance in both segmentation and grading tasks, attaining dice coefficient scores (DSC) of 86.13, 86.75, and 92.41 in enhancing tumor, tumor core, and whole tumor, respectively, alongside 98.49% classification accuracy for high- and low-grade glioma. Experimental findings validate the superior capabilities of the proposed framework compared to the existing methods. These results highlight the potential of the proposed model to aid radiologists in achieving accurate and reliable diagnoses, improving patient outcomes, and supporting clinical decision-making.