Diagnosis of Alzheimer's disease using structure highlighting key slice stacking and transfer learning

Background In recent years, two-dimensional convolutional neural network (2D CNN) have been widely used in the diagnosis of Alzheimer's disease (AD) based on structural magnetic resonance imaging (sMRI). However, due to the lack of targeted processing of the key slices of sMRI images, the classification performance of the CNN model needs to be improved. Purpose Therefore, in this paper, we propose a key slice processing technique called the structural highlighting key slice stacking (SHKSS) technique, and we apply it to a 2D transfer learning model for AD classification. Methods Specifically, first, 3D MR images were preprocessed. Second, the 2D axial middle-layer image was extracted from the MR image as a key slice. Then, the image was normalized by intensity and mapped to the red, green, and blue (RGB) space, and histogram specification was performed on the obtained RGB image to generate the final three-channel image. The final three-channel image was input into a pretrained CNN model for AD classification. Finally, classification and generalization experiments were conducted to verify the validity of the proposed method. Results The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set show that our SHKSS method can effectively highlight the structural information in MRI slices. Compared with existing key slice processing techniques, our SHKSS method has an average accuracy improvement of at least 26% on the same test data set, and it has better performance and generalization ability. Conclusions Our SHKSS method not only converts single-channel images into three-channel images to match the input requirements of the 2D transfer learning model but also highlights the structural information of MRI slices to improve the accuracy of AD diagnosis.