Deep Learning Architecture Reduction for fMRI Data.

ABSTRACT: In recent years, deep learning models have demonstrated an inherently better ability to tackle non-linear classification tasks, due to advances in deep learning architectures. However, much remains to be achieved, especially in designing deep convolutional neural network (CNN) configurations. The number of hyper-parameters that need to be optimized to achieve accuracy in classification problems increases with every layer used, and the selection of kernels in each CNN layer has an impact on the overall CNN performance in the training stage, as well as in the classification process. When a popular classifier fails to perform acceptably in practical applications, it may be due to deficiencies in the algorithm and data processing. Thus, understanding the feature extraction process provides ins

SUBMITTER: Alvarez-Gonzalez R 

PROVIDER: S-EPMC8870362 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature