  The causal explanation of image misclassifications is an understudied niche,
which can potentially provide valuable insights in model interpretability and
increase prediction accuracy. This study trains CIFAR-10 on six modern CNN
architectures, including VGG16, ResNet50, GoogLeNet, DenseNet161, MobileNet V2,
and Inception V3, and explores the misclassification patterns using conditional
confusion matrices and misclassification networks. Two causes are identified
and qualitatively distinguished: morphological similarity and non-essential
information interference. The former cause is not model dependent, whereas the
latter is inconsistent across all six models.
  To reduce the misclassifications caused by non-essential information
interference, this study erases the pixels within the bonding boxes anchored at
the top 5% pixels of the saliency map. This method first verifies the cause;
then by directly modifying the cause it reduces the misclassification. Future
studies will focus on quantitatively differentiating the two causes of
misclassifications, generalizing the anchor-box based inference modification
method to reduce misclassification, exploring the interactions of the two
causes in misclassifications.
