Diagnosis of Alzheimer's disease using transfer learning with multi-modal 3D Inception-v4.

Background: Deep learning (DL) technologies are playing increasingly important roles in computer-aided diagnosis in medicine. In this study, we sought to address issues related to the diagnosis of Alzheimer's disease (AD) based on multi-modal features, and introduced a multi-modal three-dimensional Inception-v4 model that employs transfer learning for AD diagnosis based on magnetic resonance imaging (MRI) and clinical score data. Methods: The multi-modal three-dimensional (3D) Inception-v4 model was first pre-trained using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Subsequently, independent validation data were used to fine-tune the model with pre-trained weight parameters. The model was quantitatively evaluated using the mean values obtained from five-fold cross-validation. Further, control experiments were conducted to verify the performance of the model patients with AD, and in the study of disease progression. Results: In the AD diagnosis task, when a single image marker was used, the average accuracy (ACC) and area under the curve (AUC) were 62.21% and 71.87%, respectively. When transfer learning was not employed, the average ACC and AUC were 75.74% and 83.13%, respectively. Conversely, the combined approach proposed in this study achieved an average ACC of 87.84%, and an average AUC of 90.80% [with an average precision (PRE) of 87.21%, an average recall (REC) of 82.52%, and an average F1 of 83.58%]. Conclusions: In comparison with existing methods, the performance of the proposed method was superior in terms of diagnostic accuracy. Specifically, the method showed an enhanced ability to accurately distinguish among various stages of AD. Our findings show that multi-modal feature fusion and transfer learning can be valuable resources in the treatment of patients with AD, and in the study of disease progression.