  In recent years, artificial intelligence is increasingly being applied widely
in many different fields and has a profound and direct impact on human life.
Following this is the need to understand the principles of the model making
predictions. Since most of the current high-precision models are black boxes,
neither the AI scientist nor the end-user deeply understands what's going on
inside these models. Therefore, many algorithms are studied for the purpose of
explaining AI models, especially those in the problem of image classification
in the field of computer vision such as LIME, CAM, GradCAM. However, these
algorithms still have limitations such as LIME's long execution time and CAM's
confusing interpretation of concreteness and clarity. Therefore, in this paper,
we propose a new method called Segmentation - Class Activation Mapping (SeCAM)
that combines the advantages of these algorithms above, while at the same time
overcoming their disadvantages. We tested this algorithm with various models,
including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual
Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm
has met all the requirements for a specific explanation in a remarkably concise
time.
