Fortunately, emerging trends in AI have revolutionized how we think and provided wide scopes of applications in electrochemistry, imaging processing, and material science. Machines can be trained to interpret images similarly to our brains and analyze them much more thoroughly than humans. For example, deep learning is a rapidly evolving form of AI that uses deep convolutional neural networks (CNN) algorithm to mimic the human brain and has been prominently used to identify facial features, text, and voice. The CNN-based algorithm has been used to classify the medical images in clinical treatment and teaching tasks (e.g., pneumonia). CNN automatically extracts the features of the pictures of the hidden network with raw-image input data. VGG16 model belongs to the CNN family. It has a superficial layer architecture, but in this case, can provide higher accuracy on the dataset, and it is suitable for small data sets that only contain a few thousand images. In this work, deep learning was used to select the good quality fused images from thousands of merged pictures. In the future, AI-assisted fused SECM images may be interpreted with more value and contribute to the early detection of cancers.