Scholarlink

<p>In medicinal Computer Assisted Diagnosis (CAD) systems, the automatic classification of brain tumors plays a substantial role, as misclassifications can have considerable implications for a patient’s chances of survival. To enhance the performance of classification analysis, we suggested a brain tumor classification model based on Convolutional Neural Network (CNN) pre-trained models by including some additional layers for feature extraction with three diverse activation functions (Rectified Linear Unit (ReLU), Parametric Rectified Linear Unit (PReLU), and Swish).We analyzed seven pre-trained models such as VGG19, InceptionV3, ResNet50V2, InceptionResNetV2, DenseNet201, MobileNetV2, and EfficientNetB7, with additional layers for feature extraction. In order to get more precise outcomes and consistent results, we designed an ensemble algorithm using a majority voting scheme.We trained and tested our proposed architecture using the ’Brain MRI Images for Brain Tumor Detection’ dataset. Our model attained a 99.34% classification accuracy on the Brain Magnetic Resonance Imaging (MRI) images for Brain Tumor Detection dataset and an Area Under Curve (AUC), Precision, Recall, and F1-score of 0.9841, 1.0, 0.9843 and 0.9921 respectively. These results illustrate the suggested system’s efficiency in enhancing the classification rate and reducing the misclassification rate. It also demonstrates that the proposed model is a promising method for automatically classifying brain tumors. The increased accuracy and performance metrics suggest its potential usefulness in real-world medical applications, ultimately leading to enhanced patient outcomes.</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

Brain tumor classification: a blend of ensemble learning and fine-tuned pre-trained models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月28日

查看全文
  • Soumyarashmi Panigrahi,
  • Dibya Ranjan Das Adhikary,
  • Binod Kumar Pattanayak

摘要

In medicinal Computer Assisted Diagnosis (CAD) systems, the automatic classification of brain tumors plays a substantial role, as misclassifications can have considerable implications for a patient’s chances of survival. To enhance the performance of classification analysis, we suggested a brain tumor classification model based on Convolutional Neural Network (CNN) pre-trained models by including some additional layers for feature extraction with three diverse activation functions (Rectified Linear Unit (ReLU), Parametric Rectified Linear Unit (PReLU), and Swish).We analyzed seven pre-trained models such as VGG19, InceptionV3, ResNet50V2, InceptionResNetV2, DenseNet201, MobileNetV2, and EfficientNetB7, with additional layers for feature extraction. In order to get more precise outcomes and consistent results, we designed an ensemble algorithm using a majority voting scheme.We trained and tested our proposed architecture using the ’Brain MRI Images for Brain Tumor Detection’ dataset. Our model attained a 99.34% classification accuracy on the Brain Magnetic Resonance Imaging (MRI) images for Brain Tumor Detection dataset and an Area Under Curve (AUC), Precision, Recall, and F1-score of 0.9841, 1.0, 0.9843 and 0.9921 respectively. These results illustrate the suggested system’s efficiency in enhancing the classification rate and reducing the misclassification rate. It also demonstrates that the proposed model is a promising method for automatically classifying brain tumors. The increased accuracy and performance metrics suggest its potential usefulness in real-world medical applications, ultimately leading to enhanced patient outcomes.