Abstract #0602

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

Moran Artzi^1,2,3, Erez Redmard³, Oron Tzemach³, Jonathan Zeltser³, Omri Gropper⁴, Jonathan Roth^2,5,6, Ben Shofty^2,5,7, Danil A. Kozyrev^5,7, Shlomi Constantini^2,5,7, and Liat Ben-Sira^2,8

¹Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ²Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel, ³Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, ⁴The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel, ⁵Department of Pediatric Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ⁶The Gilbert Israeli Neurofibromatosis Center, Tel Aviv University, Tel Aviv, Israel, ⁷The Gilbert Israeli Neurofibromatosis Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ⁸Division of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

A fused architecture contrived of 2 neural networks, pre-trained ResNet-50 CNN and tabular based network is proposed for the classification of Posterior fossa tumors (PFT) types. The study included data for 158 MRI of healthy controls and pediatric patients with PFT. The input data were T1WI+C, FLAIR and diffusion MRI, and tabular data (subject's age). The best classification results obtained by the fused CNN + tubular data architecture and based on diffusion images, achieved cross-validation accuracy of validation=0.88±0.04, test=0.87±0.02. Overall, the proposed architecture achieved a ~16% improvement in accuracy for the test data compared to CNN method for this dataset.

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