Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.

As a common malignant disease, brain tumor has high mortality. The automatic segmentation of brain tumor has significance for clinical diagnosis and surgery treatment. With the development of deep learning, CNN (Convolutional Neural Network) achieves remarkable performance in image processing and computer vision. Researchers have proposed a large number of CNN-based segmentation models such as FCN (Fully Convolutional Network) and Unet from the perspective of network architecture, loss function and attention mechanism. However, most of them are based on the traditional pooling operations such as average pooling and maximum pooling, which will lead to the loss of significant features or average features. Especially in brain tumor segmentation, tissues are usually quite small, so feature losing is more serious. More importantly, the fixed pooling patterns such as maximum pooling and average pooling, which cannot accommodate to varying data, may not be able to accurately express their features in down-sampling. In this study, we first unify maximum pooling and average pooling, and then propose a novel generalized pooling (GP) method with adaptive weights. This is the first work to improve models from the perspective of pooling operations for brain tumor segmentation. The experimental results show that our generalized pooling method is effective to segment brain tumors, outperforming the traditional pooling methods.