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Abstract #0602

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

Moran Artzi1,2,3, Erez Redmard3, Oron Tzemach3, Jonathan Zeltser3, Omri Gropper4, Jonathan Roth2,5,6, Ben Shofty2,5,7, Danil A. Kozyrev5,7, Shlomi Constantini2,5,7, and Liat Ben-Sira2,8
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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