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This paper proposed a new method for classification of brain tumors based on the integration of two prominent deep learning techniques namely, CNN and SVM. The integrated CNN-SVM scheme, which is to be implemented in the MRI image datasets, will thus seek to optimize the accuracy and the reliability of the diagnosis of ABT. In our approach, we first employ a DCNN to extract distinctive features from the given images, and then we use the SVM to classify images effectively. The experiment results on two well-known MRI datasets base shown that the fusion model of CNN-SVM is superior to the CNN and SVM model and shows a higher accuracy rate and better generality. Therefore, this research makes a contribution to advancing studies in clinical diagnoses when using I deep learning and classical machine learning approaches in the diagnosis of brain tumours more accurately and timely.

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Advancements in Brain Tumor Classification: Unveiling the Power of CNN-SVM Fusion for MRI Datasets

  • 会议论文

  • 发表日期：

    2026年

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  • Sankara Narayanan Rajapandian,
  • Senthil Kumar Murugesan,
  • Chidhambararajan Balasubramanian,
  • Susee Sundaraja Kannan

摘要

This paper proposed a new method for classification of brain tumors based on the integration of two prominent deep learning techniques namely, CNN and SVM. The integrated CNN-SVM scheme, which is to be implemented in the MRI image datasets, will thus seek to optimize the accuracy and the reliability of the diagnosis of ABT. In our approach, we first employ a DCNN to extract distinctive features from the given images, and then we use the SVM to classify images effectively. The experiment results on two well-known MRI datasets base shown that the fusion model of CNN-SVM is superior to the CNN and SVM model and shows a higher accuracy rate and better generality. Therefore, this research makes a contribution to advancing studies in clinical diagnoses when using I deep learning and classical machine learning approaches in the diagnosis of brain tumours more accurately and timely.