Alzheimer’s disease diagnosis method based on convolutional neural network using key slices voting

With the wide application of convolutional neural networks in the field of computer vision, its application in medical image analysis has become a research hotspot. Due to the significant social impact of Alzheimer's disease, the detection of Alzheimer's disease in magnetic resonance imaging data has become the focus of research. At present, researchers have found that a large number of studies have data leakage problems, resulting in a poor generalization of the training model. On the Alzheimer's Disease Neuroimaging Initiative database, this paper conducts comparative experiments based on the random split of slices and the independent split of subjects and verifies the existence of data leakage problem. Furthermore, based on the independent data slices of the subjects, this paper innovatively proposes an auxiliary diagnosis method of Alzheimer's disease based on the slice voting algorithm and uses different slice intervals of the coronal plane to train the model, as well as 3D magnetic resonance imaging data to train the 3D model convolutional neural network model, experimental results show that the accuracy of the proposed method is 93.10%, which is 5.39% higher than the slice recognition rate, and 4.59% higher than that of 3D magnetic resonance imaging. At the same time, the experimental results show that the slice interval of [75, 106] has the best effect on the diagnosis of Alzheimer's disease. The experimental results of this paper have an important role in the auxiliary diagnosis of Alzheimer's disease.