# Advances in Computer-Aided Medical Image Processing
## 3 Medical Image Processing
### Intelligent Image Recognition
#### 3.2.2 Intelligent Algorithm for Medical Image Recognition after Image Enhancement  
In Section 3.2.1, for the defects of medical images in different parts, we introduce the network model designed. In this section, we present the transfer learning method of medical images introduced due to the lack of a tumor dataset and the attention mechanism of medical images introduced due to the excessive noise of medical images and the inconspicuous details of some medical images. When the image is not multimodal fusion when the spatial information loss is due to the introduction of cascading strategies, the introduction of these strategies is analyzed, including the basic network model and dataset, to achieve a good effect. Transfer learning: Faced with the problem of a small tumor dataset in the same site, people began to use transfer learning for tumor identification. Since tumors with internal structures in the human body are the result of abnormal cells, the abnormal images with mutations are similar. Therefore, people can learn more features by using transfer learning [69], use high-quality tumor data with similar features to pretrain the model, and then fine-tune the parameters of the convolution layer to migrate the model to the tumor site where data are scarce to improve the generalization performance of the model. [PERSON] [64] took ResNet50 as the basic network framework, used transfer learning to realize the model function, and tested it on the BreaKHis dataset, and the accuracy rate converged to 98%. [PERSON] et al. [31] adopted the transfer learning ResNet50 network, formed two symmetric source domain and target domain conversion subnetworks by sharing encoders and two dedicated decoders, and proposed the deeply symmetric UDA architecture. The MMWHS2017 challenge heart dataset [45] was adopted, and the segmentation accuracy reached 78.50%. [PERSON] et al. [37] adopted the transfer learning method; used GoogLeNet, ResNet, and VGGNet to extract image features; input them into the fully connected layer; and used mean pool classification to classify benign and malignant cells. The standard benchmark dataset [52] was used to construct the dataset with the image of the LRH Hospital in Peshawar, Pakistan, and the accuracy rate reached 97.525%. [PERSON] et al. [46] adopted the transfer learning method; used ResNet, AlexNet, and GoogLeNet models to extract image features; used the softmax function to classify the full connectivity layer; and classified the lung CT images of 2933 patients with COVID-19. The classification accuracy rate reached 99.054%. [PERSON] and [PERSON] [70] adopted the transfer learning method and optimized the methods of ADADADDelta, ADAM, RMSprop, and SGD by using the VGG16, VGG19, ResNet50, and DenseNet21 networks. When tested on the dataset of [PERSON] et al. 's [71] published Figshare [46], the accuracy rate of the optimization method with the ResNet network and Adadelta reached 99.02%, and the accuracy rate with the DenseNet21 network was 98.91%. Scale attention (SA): Since medical images are multimodal and have many problems, such as noise, poor contrast, and obscure details, the attention module can capture low-level features and high-level semantic features of all scales so that the weight of each feature channel can be adjusted adaptively, and unimportant scale information can be suppressed, which can not only eliminate irrelevant and noisy features but also reduce unnecessary parameters. At the same time, it can highlight the local features and improve the accuracy of the model. Importantly, the scale attention mechanism also aids in model interpretability. By highlighting the most significant features contributing to a prediction, it provides insights into the model's decision-making process, which in turn fosters a better understanding and trust among clinicians. [PERSON] et al. [23] embedded the attention module SE (Squeeze-and-Excitation) into U-Net for the MRI glioma segmentation task. Multiview fusion technology is proposed to obtain the 3D image context information on a 2D model.