On the design of convolutional neural networks for automatic detection of Alzheimer's disease
This work addresses early detection of Alzheimer's disease for medical diagnosis, but it is incremental as it focuses on optimizing existing CNN techniques.
The paper tackled the problem of early detection of Alzheimer's disease using structural brain MRI scans, achieving an approximately 14% increase in test accuracy over existing models when distinguishing between patients with AD, mild cognitive impairment, and controls in the ADNI dataset.
Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural networks (CNNs) trained to detect AD using structural brain MRI scans. Specifically, we provide evidence that (1) instance normalization outperforms batch normalization, (2) early spatial downsampling negatively affects performance, (3) widening the model brings consistent gains while increasing the depth does not, and (4) incorporating age information yields moderate improvement. Together, these insights yield an increment of approximately 14% in test accuracy over existing models when distinguishing between patients with AD, mild cognitive impairment, and controls in the ADNI dataset. Similar performance is achieved on an independent dataset.