The invention provides a breast lump segmentation method based on channel-guided double-
pooling multi-
scale space attention, and aims to solve the problems of small area,
low contrast, fuzzy boundary and the like of
breast lumps in an X-
ray image, the network extracts full-view image features and compresses space dimensions through a deep residual
encoder; and realizing fusion of low-level details and high-level semantic features in the decoder by means of jump connection. A double-
pooling gating mechanism is built in the decoder, channel guide weights are generated in parallel through global average
pooling and maximum pooling,
lesion significant features are screened in a self-adaptive mode, and redundant backgrounds are restrained; the multi-
scale space attention module captures multi-
scale space information through multi-
branch large-kernel separable
convolution, generates a space attention graph, combines the space attention graph with channel weights
element by element, and accurately focuses a
lesion area and a boundary. Experiments show that the Dice coefficients of the method on INbreast, CBIS-DDSM and private In-home data sets respectively reach 90.94%, 80.60% and 84.50%, the method is superior to a mainstream method, the segmentation precision and generalization ability are improved, and reliable support is provided for early screening and computer-
aided diagnosis of
breast cancer.