CBAM MECHANISM-BASED RESIDUAL NETWORK MEDICAL IMAGE AUXILIARY DETECTION METHOD

A CBAM mechanism-based residual network medical image auxiliary detection method, relating to the technical field of medical image detection. The method comprises the following steps of: S1, acquiring a medical image (a lung cxr medical image), and clipping and normalizing the medical image; S2, performing data transformation on the normalized medical image; S3, on the basis of convolutional autoencoding, a combined spatial and channel attention mechanism feature extraction method, and an hs-block module, establishing a network model; and S4, inputting into the network model for prediction the medical image subjected to the data transformation, and visualizing a focus prediction region. By means of introducing a cbam mechanism and introducing an hs-block residual structure, the method enhances the lung X-ray feature extraction capability of the model, and improves the detection accuracy; and the method is used for assisting traditional manual screening of lung X-ray films, and can improve the detection effici