Diagnosis of Alzheimer's Disease Based on Deeply-Fused Nets

Abstract

Aim and objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of the automatic diagnosis of AD.

Materials and methods: MRI image data from the Alzheimer's Disease Neuroimaging Initiative were used to train a deep learning model to achieve a computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity.

Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard based network.

Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

Keywords: Alzheimer's disease; Deep convolutional network; accuracy of automatic diagnosis; diagnosis; fused net; single standard base network.