Considering that the current neural ne tworks have some problems in brain tumor magnetic resonance imaging (MRI) segmentation,which are a large numb er of parameters and low accuracy of small target segmentation, an improved lightweight brain tumor segmentation network multiple fiber residual-like networks (MF-RES2Net) is proposed .The network is based on 3D U-Net and replaces the traditional convolution module with the multi-fiber (M F) unit and the RES2 unit.The MF unit mixes the channels of the feature image,which increases the com munication between channels.The RES2 unit divides the channels equally,and the convolution result of one single channel is added to the adjacent channels,which expands the image recept ive field and reduces the parameters while retaining feature details.In addition,a improved weighted-loss funct ion is proposed to address the network segmentation accuracy of small targets for the data imbalanc e problem. MF-RES2Net is verified on the BRATS 2019 data set,and the average Dice coeffic ients of tumor segmentation in whole tumor,core tumor and enhanced tumor region have reached 8 9.98%,84.02%, 77.62% respectively,and the network has 3.16 M parameters and 16.24 G FLOPs.The re sult shows MF-RES2Net achieves more accurate target segmentation with lower parameters and calculations, effectively reducing equipment requirements during network running.