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

TL;DR

This paper evaluates how CNN training parameters affect accuracy in Alzheimer's detection from MRI scans and compares visualization methods to interpret model decisions.

Contribution

It systematically assesses CNN hyper-parameters' impact on accuracy and compares visualization algorithms for interpretability in medical imaging.

Findings

Hyper-parameters significantly influence CNN accuracy.

Certain visualization methods provide clearer, more focused explanations.

Comparison reveals strengths and limitations of visualization techniques.

Abstract

Convolutional neural networks (CNN) have become a powerful tool for detecting patterns in image data. Recent papers report promising results in the domain of disease detection using brain MRI data. Despite the high accuracy obtained from CNN models for MRI data so far, almost no papers provided information on the features or image regions driving this accuracy as adequate methods were missing or challenging to apply. Recently, the toolbox iNNvestigate has become available, implementing various state of the art methods for deep learning visualizations. Currently, there is a great demand for a comparison of visualization algorithms to provide an overview of the practical usefulness and capability of these algorithms. Therefore, this thesis has two goals: 1. To systematically evaluate the influence of CNN hyper-parameters on model accuracy. 2. To compare various visualization methods with respect to the quality (i.e. randomness/focus, soundness).