In recent years, machine learning methods have been extensively studied in Alzheimer's disease (AD) prediction. Most existing methods extract the handcraft features from images and then train a classifier for prediction. Although it has good performance, it has some deficiencies in essence, such as relying too much on image preprocessing, easily ignoring the latent lesion features. This paper proposes a deep learning network model based on the attention mechanism to learn the latent features of PET images for AD prediction. Firstly, we design a novel backbone network based on ResNet18 to capture the potential features of the lesion and avoid the problems of gradient disappearance and gradient explosion. Secondly, we add the channel attention mechanism so that the model can learn to use global information to selectively emphasize information features and suppress low-value features, which is conducive to the extraction of semantic features. Finally, we expand the data by horizontal flipping and random flipping, which reduces the over-fitting phenomenon caused by the limited medical data set and improves the generalization ability of the model. This method is evaluated on 238 brain PET images collected in the ADNI database, and the prediction accuracy is 94.2%, which is better than most mainstream algorithms.
Digital image quality is disturbed by noise to some extent. Researchers proposed a series of wavelet transform, non-local mean, and partial differential equation denoising algorithms to obtain high-quality images for subsequent research. Removing noise and preserving the edges and details of the image has attracted wide publicity. Methods based on anisotropic diffusion models have recently gained popularity, but these lead to over-smooth the image details. In this paper, we propose an improved denoising algorithm based on the anisotropic diffusion model. Our method further modifies the diffusion coefficient of the denoising model based on fractional differential operator and Gauss curvature (FDOGC). We use the edge-preserving characteristic of bilateral filtering to recover the image texture and adjust the diffusion coefficient given the characteristics of local variance. To balance the performance of denoising and edge-preserving, we add a regularization term to the diffusion model. We conduct ablation studies to verify the effectiveness of the innovation points. Our method can adjust the counterpoise between noise removal and edge preservation. Extensive experiments on public standard datasets indicate the superiority of our algorithm, in terms of not only quantitative and qualitative evaluation but also better visual effects.
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