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<p>To enhance patient longevity and accurately diagnose life threatening diseases like brain tumors, the initial step of tumor classification holds immense importance. Medical imaging technologies are instrumental in identifying pathological conditions within the brain, with Magnetic Resonance Imaging (MRI) being widely preferred due to its superior image value and non-ionizing radiation properties. The integration of deep learning a subset of artificial intelligence has significantly propelled the advancement of brain tumor detection from MRI scans resulting in enhanced prediction rates. Among the various deep learning algorithms, the Convolutional Neural Network (CNN) is extensively employed for brain tumor analysis and classification. In this study we conduct a comparative performance analysis of transfer learning-based CNN models specifically ResNet152 and Inception v3 for the automatic prediction of tumor cells (cancerous) within the brain. The pretrained models are trained and tested using a dataset consisting of 1080 images. Our research focuses on utilizing the ResNet152 and Inception v3 pretrained CNN models to classify brain tumors into cancerous (with tumors) and non-cancerous (without tumors). The performance of the models is evaluated using metrics such as accuracy, sensitivity, specificity and the F-measure. The outcomes of our study demonstrate that the Inception v3 pre-trained model exhibits highly accurate results showcasing improved accuracy rates, sensitivity, specificity and f measure thereby indicating its efficiency in brain tumor classification.</p>

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Brain tumor classification on MRI images using fine-tuned deep learning models

  • 期刊论文

  • 发表日期：

    2025年5月24日

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  • Manjunath R. V.,
  • Yashaswini Gowda N.,
  • Manu H. M.,
  • Bharath K. N.,
  • Manasa R.

摘要

To enhance patient longevity and accurately diagnose life threatening diseases like brain tumors, the initial step of tumor classification holds immense importance. Medical imaging technologies are instrumental in identifying pathological conditions within the brain, with Magnetic Resonance Imaging (MRI) being widely preferred due to its superior image value and non-ionizing radiation properties. The integration of deep learning a subset of artificial intelligence has significantly propelled the advancement of brain tumor detection from MRI scans resulting in enhanced prediction rates. Among the various deep learning algorithms, the Convolutional Neural Network (CNN) is extensively employed for brain tumor analysis and classification. In this study we conduct a comparative performance analysis of transfer learning-based CNN models specifically ResNet152 and Inception v3 for the automatic prediction of tumor cells (cancerous) within the brain. The pretrained models are trained and tested using a dataset consisting of 1080 images. Our research focuses on utilizing the ResNet152 and Inception v3 pretrained CNN models to classify brain tumors into cancerous (with tumors) and non-cancerous (without tumors). The performance of the models is evaluated using metrics such as accuracy, sensitivity, specificity and the F-measure. The outcomes of our study demonstrate that the Inception v3 pre-trained model exhibits highly accurate results showcasing improved accuracy rates, sensitivity, specificity and f measure thereby indicating its efficiency in brain tumor classification.