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Brain tumors lead to a severe medical concern characterized by their heterogeneity and complex behavior. Timely diagnosis is crucial for effective treatment of brain tumor before it brings deadly consequences. In recent time, use of deep learning techniques are ubiquitous in healthcare industries for developing automated system for disease prediction and diagnosis. This study highlights the use of different image augmentation techniques augmented with pre-trained models like VGG-16, ResNet-50, Inception V3, and MobileNet V2 for the identification and prevention of brain tumor. The proposed architecture uses brain MRI images which are classified as tumor or no tumor. Results confirm that the performance of pre-trained model architectures is enhanced significantly by applying image augmentation and balancing technique. Leveraging image augmentation technique, ResNet-50 model outperformed all other pre-trained models. It achieved accuracy, recall, precision, and f1 score of 99.12%, 98.89%, 98.56%, and 98.72% respectively. Future work will focus on building sophisticated deep learning models using multi-modal data and advanced image pre-processing technique to ensure more accurate and reliable decision making system for brain tumor classification.

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Brain Tumor Classification Using CNN Based Pre-trained Model Architecture Leveraging Image Augmentation Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Tapaswini Sahoo,
  • Sourav Kumar Giri,
  • Sujata Dash,
  • Akangjungshi Longkumer

摘要

Brain tumors lead to a severe medical concern characterized by their heterogeneity and complex behavior. Timely diagnosis is crucial for effective treatment of brain tumor before it brings deadly consequences. In recent time, use of deep learning techniques are ubiquitous in healthcare industries for developing automated system for disease prediction and diagnosis. This study highlights the use of different image augmentation techniques augmented with pre-trained models like VGG-16, ResNet-50, Inception V3, and MobileNet V2 for the identification and prevention of brain tumor. The proposed architecture uses brain MRI images which are classified as tumor or no tumor. Results confirm that the performance of pre-trained model architectures is enhanced significantly by applying image augmentation and balancing technique. Leveraging image augmentation technique, ResNet-50 model outperformed all other pre-trained models. It achieved accuracy, recall, precision, and f1 score of 99.12%, 98.89%, 98.56%, and 98.72% respectively. Future work will focus on building sophisticated deep learning models using multi-modal data and advanced image pre-processing technique to ensure more accurate and reliable decision making system for brain tumor classification.