Brain Tumor Classification Using Convolutional Neural Network

Using our simple architecture and without any prior region-based segmentation, we could achieve a training accuracy of 98.51% and validation accuracy of 84.19% at best

Nyoman Abiwinanda

2018

Scholarcy highlights

  • Misdiagnosis of brain tumor types will prevent effective response to medical intervention and decrease the chance of survival among patients
  • The CNN was trained on a brain tumor dataset consisting of 3064 T-1 weighted CE-MRI images publicly available via figshare Cheng)
  • Using our simple architecture and without any prior region-based segmentation, we could achieve a training accuracy of 98.51% and validation accuracy of 84.19% at best
  • These figures are comparable to the performance of more complicated region-based segmentation algorithms, which accuracies ranged between 71.39 and 94.68% on identical dataset Cheng, Cheng et al
  • IEEE Transactions on Medical Imaging, 35(5), 1240–1251.Google Scholar

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