\MCADNNet\: Recognizing Stages of Cognitive Impairment through Efficient Convolutional \fMRI\ and \MRI\ Neural Network Topology Models
IEEE Access, (октября 2019)Аннотация
Mild cognitive impairment (MCI) represents the intermediate
stage between normal cerebral aging and dementia associated with
Alzheimer's disease (AD). Early diagnosis of MCI and AD through
artificial intelligence has captured considerable scholarly
interest; researchers hope to develop therapies capable of
slowing or halting these processes. We developed a
state-of-the-art deep learning algorithm based on an optimized
convolutional neural network (CNN) topology called MCADNNet that
simultaneously recognizes MCI, AD, and normally aging brains in
adults over the age of 75 years, using structural and functional
magnetic resonance imaging (fMRI) data. Following highly
detailed preprocessing, four-dimensional (4D) fMRI and 3D MRI
were decomposed to create 2D images using a lossless
transformation, which enables maximum preservation of data
details. The samples were shuffled and subject-level training
and testing datasets were completely independent. The optimized
MCADNNet was trained and extracted invariant and hierarchical
features through convolutional layers followed by
multi-classification in the last layer using a softmax layer. A
decision-making algorithm was also designed to stabilize the
outcome of the trained models. To measure the performance of
classification, the accuracy rates for various pipelines were
calculated before and after applying the decision-making
algorithm. Accuracy rates of 99.77\% 0.36\% and 97.5\% 1.16\%
were achieved for MRI and fMRI pipelines, respectively, after
applying the decision-making algorithm. In conclusion, a
cutting-edge and optimized topology called MCADNNet was designed
and preceded a preprocessing pipeline; this was followed by a
decision-making step that yielded the highest performance
achieved for simultaneous classification of the three cohorts
examined.