Multi-scale methodology can enhance the performance of the model in deep learning. The current multi-scale methodology focuses on changing the formation, which will increase the parameters and calculations of the network. This paper offers a multi-scale method for data based on bit plane slicing(MBB). This expands the receptive field of valid information in image data. It is done by multi-level fusing image with high bit planes. Our experimentation shows that by adding MBB in front of the backbone network, one can achieve a significant performance improvement. The MBB approach is widely applicable because it does not require changes to the structure of the backbone network.