FS-FCN: a frequency-spatial fusion convolutional network for brain tumor classification in MR images.

Introduction: Brain tumor classification using magnetic resonance imaging (MRI) is important for computer-aided medical image analysis. However, different brain tumor types may exhibit highly similar grayscale patterns, textures, and boundaries. Conventional convolutional neural networks rely primarily on spatial-domain convolution and do not explicitly model frequency-domain information. Moreover, high-frequency details, such as edges and textures, are often weakened by multi-stage downsampling, while interaction between spatial- and frequency-domain features remains limited. Consequently, fine-grained differences among tumor categories may not be effectively captured. Methods: To address these limitations, a Frequency-Spatial Fusion Convolutional Network (FS-FCN) was developed for brain tumor MRI classification. A Parametric Wavelet Downsampling (PWD) module and a Frequency-Spatial Convolution (FSConv) module were incorporated into the convolutional network. Spatial- and frequency-domain information was jointly modeled during multilevel encoding and deep feature learning to improve the preservation and discriminative representation of key structures, local textures, and edge details. Results: On the Cheng Brain Tumor MRI Three-Class Classification Dataset, FS-FCN achieved an average classification accuracy of 98.53% ± 0.35% using five-fold cross-validation. The applicability of the model across different classification settings was further evaluated through a four-class classification experiment. Discussion: The results demonstrate that jointly modeling spatial- and frequency-domain information improves the representation of brain tumor MRI features and provides an effective feature-learning approach for brain tumor MRI classification.