To classify three types of brain CT (computerized tomography) images in Alzheimer's disease

lesion (e.g.

brain tumor) and healthy aging

an improved ResNet-10 convolutional neural network is proposed in this papers. A residual hybrid attention module is embedded in the residual identity mapping to capture the location and content information of brain tissue in brain CT images

solving the original model to extract weak distinguish features problems. In addition

to simplify the improved model and alleviate the overfitting

several techniques such as global average pooling and Dropout are used in the model. Moreover

to have strong generalization ability in the case of limited sample quantity

tag smoothing cross-entropy loss function is adopted to train the model. Experimental results show that the improved ResNet-10 achieves 97.47% accuracy in classifying brain CT images.