Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification.

This paper focused on the problem of diagnosis of Alzheimer’s disease via the combination of deep learning and radiomics methods. We proposed a classification model for Alzheimer’s disease diagnosis based on improved convolution neural network models and image fusion method and compared it with existing network models. We collected 182 patients in the ADNI and PPMI database to classify Alzheimer’s disease, and reached 0.906 AUC in training with single modality images, and 0.941 AUC in training with fusion images. This proved the proposed method has better performance in the fusion images. The research may promote the application of multimodal images in the diagnosis of Alzheimer’s disease. Fusion images dataset based on multi-modality images has higher diagnosis accuracy than single modality images dataset. Deep learning methods and radiomics significantly improve the diagnosing accuracy of Alzheimer’s disease diagnosis.