基于多尺度通道注意力的特征融合皮肤病分割方法OA

In view of the problem of low image segmentation accuracy caused by factors such as irregular shapes,blurred edges,and foreign body occlusion of melanoma,this study proposes a MSFFNet(Multi Scale Feature Fusion Network).In the encoding stage,a pyramid split attention module is introduced to expand the receptive field and capture feature information at different scales.The channel attention mechanism is used to recalibrate the weights of different channels,and then point-by-point multiplication fusion is performed with the original feature space.The Dice Loss function is adopted for end-to-end optimization to alleviate the negative impact of class imbalance in sam-ples and further improve the segmentation performance of the network model.The segmentation performance of the model is verified on the ISIC2018 skin disease image dataset.Experimental results show that the Dice and IoU(Inter-section over Union)of the proposed method are 89.40%and 82.27%respectively.Compared with mainstream seg-mentation algorithms,the proposed segmentation method is more similar to the results of manual segmentation by doc-tors.