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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