Feature Fusion Alzheimer’s Disease Classification Algorithm Based on Transfer Learning

In order to solve the problem that the pathological features of Alzheimer’s disease (AD) MRI images are slightly different from normal tissue morphology and are easily affected by irrelevant areas in classifica-tion diagnosis, an AD classification algorithm based on transfer learning feature fusion is proposed by an-alyzing MRI images to realize AD classification judgment and disease prediction. First, the information features of MRI images are extracted from the spatial domain and frequency domain respectively; then, the coronal slices in the anatomical view of the ADNI dataset are used to improve the accuracy of the AD bi-nary classification task; then, the improved fusion Vision Transformer and GFNet structure and transfer learning technology are combined to improve the robustness of the network model; finally, the majority voting mechanism of the coronal slice image data of a single patient is used to reduce the contingency and uncertainty of the classification process, improve the accuracy of AD binary classification task to 0.939. Comparative experiments and ablation experiments are carried out on the publicly available ADNI dataset and Kaggle dataset, which proves the potential and application value of the proposed algorithm in the early diagnosis of AD binary classification tasks.