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 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 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 base 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