Towards practical application of deep learning in diagnosis of Alzheimer's disease.

Background: Accurate diagnosis of Alzheimer's disease (AD) is both challenging and time consuming. With a systematic approach to diagnosis, steps can be taken toward improved treatment and prevention of the disease. Objective: This study explores the practical application of deep learning models for the diagnosis of AD across different disease stages. Methods: Due to computational complexity, long training times, and limited availability of labeled datasets, full brain three-dimensional (3D) convolutional neural networks (CNNs) are not commonly used, and many studies rely on two-dimensional (2D) variants. In this work, full brain 3D versions of well-known 2D CNN architectures were designed, trained, and tested for the diagnosis of multiple stages of AD. More than 1500 full brain volumes were used for model training and evaluation. Results: The proposed deep learning approach demonstrated good performance in differentiating various stages of AD. In addition to classification, the models were able to extract discriminative features relevant to disease stage. These features aligned with meaningful anatomical landmarks that are currently considered important for AD identification by clinical experts. An ensemble of all algorithms was also evaluated and achieved superior performance compared to individual models, with a maximum classification accuracy of 87.4%. Conclusions: The trained 3D CNNs and their ensemble show strong potential for assisting in the diagnosis of AD. These models may be incorporated into clinical software tools to support physicians and radiologists in improved diagnostic decision making.