  The quality and richness of feature maps extracted by convolution neural
networks (CNNs) and vision Transformers (ViTs) directly relate to the robust
model performance. In medical computer vision, these information-rich features
are crucial for detecting rare cases within large datasets. This work presents
the "Scopeformer," a novel multi-CNN-ViT model for intracranial hemorrhage
classification in computed tomography (CT) images. The Scopeformer architecture
is scalable and modular, which allows utilizing various CNN architectures as
the backbone with diversified output features and pre-training strategies. We
propose effective feature projection methods to reduce redundancies among
CNN-generated features and to control the input size of ViTs. Extensive
experiments with various Scopeformer models show that the model performance is
proportional to the number of convolutional blocks employed in the feature
extractor. Using multiple strategies, including diversifying the pre-training
paradigms for CNNs, different pre-training datasets, and style transfer
techniques, we demonstrate an overall improvement in the model performance at
various computational budgets. Later, we propose smaller compute-efficient
Scopeformer versions with three different types of input and output ViT
configurations. Efficient Scopeformers use four different pre-trained CNN
architectures as feature extractors to increase feature richness. Our best
Efficient Scopeformer model achieved an accuracy of 96.94\% and a weighted
logarithmic loss of 0.083 with an eight times reduction in the number of
trainable parameters compared to the base Scopeformer. Another version of the
Efficient Scopeformer model further reduced the parameter space by almost 17
times with negligible performance reduction. Hybrid CNNs and ViTs might provide
the desired feature richness for developing accurate medical computer vision
models
