  Deep learning, especially convolutional neural networks, has triggered
accelerated advancements in computer vision, bringing changes into our daily
practice. Furthermore, the standardized deep learning modules (also known as
backbone networks), i.e., ResNet and EfficientNet, have enabled efficient and
rapid development of new computer vision solutions. Yet, deep learning methods
still suffer from several drawbacks. One of the most concerning problems is the
high memory and computational cost, such that dedicated computing units,
typically GPUs, have to be used for training and development. Therefore, in
this paper, we propose a quantifiable evaluation method, the convolutional
kernel redundancy measure, which is based on perceived image differences, for
guiding the network structure simplification. When applying our method to the
chest X-ray image classification problem with ResNet, our method can maintain
the performance of the network and reduce the number of parameters from over
$23$ million to approximately $128$ thousand (reducing $99.46\%$ of the
parameters).
