Medical Image Information Segmentation Algorithm based on Deep Learning

In view of the high computational complexity and noise sensitivity of traditional algorithms in the current field of medical image segmentation, this paper proposes a deep medical image segmentation algorithm based on improved U-Net. This method first uses an encoder-decoder architecture to embed a channel-space dual attention mechanism to enhance the feature extraction of the target area, then realizes multi-scale feature fusion through a cross-level feature pyramid, and introduces a deep separable convolution to construct a lightweight residual module to reduce the number of parameters. At the same time, combined with the transfer learning strategy, based on the ImageNet pre-trained weights, an adaptive moment estimation optimizer and a combined loss function are used for end-to-end training, and finally the barrage-style data enhancement is used to improve the robustness of the model. Experimental verification is carried out on the BraTS brain tumor and ISIC skin lesion datasets, with a sensitivity of 93.8%, a specificity of 97.8%, and model parameters compressed to 12.4M. The inference time of a single CT image is only 0.23 seconds, providing reliable technical support for clinical intelligent diagnosis.