  We propose a feature generator backbone composed of an ensemble of
convolutional neuralnetworks (CNNs) to improve the recently emerging Vision
Transformer (ViT) models. We tackled the RSNA intracranial hemorrhage
classification problem, i.e., identifying various hemorrhage types from
computed tomography (CT) slices. We show that by gradually stacking several
feature maps extracted using multiple Xception CNNs, we can develop a
feature-rich input for the ViT model. Our approach allowed the ViT model to pay
attention to relevant features at multiple levels. Moreover, pretraining the n
CNNs using various paradigms leads to a diverse feature set and further
improves the performance of the proposed n-CNN-ViT. We achieved a test accuracy
of 98.04% with a weighted logarithmic loss value of 0.0708. The proposed
architecture is modular and scalable in both the number of CNNs used for
feature extraction and the size of the ViT.
