Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

Moran Artzi*, Erez Redmard, Oron Tzemach, Jonathan Zeltser, Omri Gropper, Jonathan Roth, Ben Shofty, Danil A. Kozyrev, Shlomi Constantini, Liat Ben-Sira

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Posterior fossa tumors (PFT) are the most common tumors in children. Differentiation between the various PFT types is critical, as different tumors have diverse treatment approaches. This study proposes the use of fused architecture comprising two neural networks, a pre-trained ResNet-50 Convolutional Neural Network (CNN) and a tabular based network for the classification of PFT. The study included data for 158 MRI scans of 22 healthy controls and 136 pediatric patients with newly diagnosed PFT (63 Pilocytic Astrocytoma, 57 Medulloblastoma and 16 Ependymoma). The input data for classification were from magnetic resonance imaging: post contrast T1-weighted, fluid attenuated inversion recovery and diffusion Trace images, and tabular data: subject's age. Evaluation of the model was performed in a stratified 5-fold cross-validation manner, based on accuracy, precision, recall and F1 score metrics. Model explanation was performed in terms of visual explanation of the CNN by Gradient-weighted Class Activation Mapping (Grad-CAM) and by testing the contribution to the classification results of the different imaging input data sets and the proposed fused architectures relative to CNN only and tabular only architectures. The best classification results were obtained with the fused CNN + tabular data architecture, and based on diffusion Trace images, achieving mean cross-validation accuracy of 0.88 ± 0.04 for the validation and 0.87 ± 0.02 for the test dataset. Overall, the proposed architecture achieved improvement in accuracy and F1 score compared to CNN method for this dataset. The source code is available on the GitHub repository:

Original languageEnglish
Article number9446147
Pages (from-to)91966-91973
Number of pages8
JournalIEEE Access
StatePublished - 2021


  • Deep learning
  • convolutional neural networks
  • posterior fossa tumors
  • tabular data


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