We expect that the fusion process can extract the details and edge of the optical , the exact cancerous area could be identified in optical after image fusion. The white arrows pointed out the cells with the highest level of EGFR expression in the fused image, which cannot be easily identified in the images that were not fused. However, trying all the parameters for image fusion is impractical since one fusion process may generate more than 4000 fused images. Hence, we have to build a machine learning model that can be used to select the fused images with better quality. After generating thousands of synthetic images, we expect the "artificial intelligence" to select the fused images with better quality, thus, enhancing the significant features in the SECM images. To achieve this goal, we need to build an ML model and train it with many images to "learn" how to distinguish images with good quality. Figure shows the ML model building process. We chose one set of pictures for building the ML model. Around 4000 fused images generated by Matlab software were labeled manually as "good quality" and "poor quality." Among them, about 3000 images were used as the training data, and 1000 images were used for testing.