Fast Training of a Convolutional Neural Network for Brain MRI Classification
The increase in AD population calls for an automated and accurate method for efficiently diagnosing AD through MRI, preferably at the stage of Early MCI (EMCI) Graphics Processing Unit (GPU) ReLU, Dropout contribute to better performance of Artificial Neural Network (ANN) resulting in a considerable decline in error rates for computer vision tasks like MRI based diagnosis through classification. CNN based architectures such as AlexNet have become dominant in image classification challenges. VGG like architectures emerged as improved variants of AlexNet but the large numbers of kernels and layers in VGG tend to slow down the training process. VoxCNN has been designed for binary AD/LMCI/EMCI/Normal classification of 3D MRI images, incorporating batch normalization, Dropout and Adam for training. Although VoxCNN output decent results with a relatively small dataset as input, the fact that 150 epochs have to be performed for each run of classification is prone to yield a lengthy training process. We tweaked Dropout probability and managed to reduce training time of VoxCNN by 49% while maintaining the same level of prediction performance in binary AD/LMCI/EMCI/Normal classification. Both ROC AUC and accuracy exhibited minor loss, i.e. 0.3% and 1.4% respectively. Standard deviation of both ROC AUC and accuracy at Epoch 75 was improved on the whole after adjustment of Dropout probability.
ACM Southeast Regional Conference