English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Convolutional neural networks (CNN) have become a powerful tool for detectingpatterns in image data. Recent papers report promising results in the domain ofdisease detection using brain MRI data. Despite the high accuracy obtained fromCNN models for MRI data so far, almost no papers provided information on thefeatures or image regions driving this accuracy as adequate methods weremissing or challenging to apply. Recently, the toolbox iNNvestigate has becomeavailable, implementing various state of the art methods for deep learningvisualizations. Currently, there is a great demand for a comparison ofvisualization algorithms to provide an overview of the practical usefulness andcapability of these algorithms.  Therefore, this thesis has two goals: 1. To systematically evaluate theinfluence of CNN hyper-parameters on model accuracy. 2. To compare variousvisualization methods with respect to the quality (i.e. randomness/focus,soundness).

Comparison of Convolutional neural network training parameters for detecting Alzheimers disease and effect on visualization