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Brain tumor segmentation is a key task in medical image analysis due to the heterogeneous and irregular nature of tumor regions. To address the limitations of existing methods in modeling long-range dependencies and reducing resource consumption, we propose a lightweight segmentation model based on a hybrid convolutional neural network (CNN) and Transformer encoder. Depthwise separable convolutions are employed in shallow layers to reduce computation, while the proposed shuffle former block (SFB) integrates Transformer and ShuffleNet v2 to effectively capture both global and local context. Furthermore, lightweightattention modules are introduced to model long-range dependencies and enhance local perception. Experimental results on the BraTS 2019 dataset demonstrate that our model achieves Dice scores of 93.1% in whole tumor (WT) , 92.2% in tumor core (TC) , and 91.2% in enhancing tumor (ET) , with only 0.98M parameters and 54.60G floating point operations per second, achieving a superior balance between segmentation accuracy and computational efficiency for deployment in resource-constrained clinical settings.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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