With over 4.3 million new cases in the U.S. every year, basal cell carcinoma (BCC), is the most common form of skin cancer. Pathologists must examine pathology images to diagnose BCC, potentially resulting in delay, error, and inconsistency. To address the need for standardized, expedited diagnosis, we created an automated diagnostic machine to identify BCC given pathology images. In MATLAB, we adapted a deep neural network image segmentation model, UNet, to train on BCC images and their corresponding masks, which can learn to highlight these nodules in pathology images by outputting a computer-generated mask. We trained the U-Net on one image from the dataset and compared the computer-generated mask output from testing on three types of images: an image from a different region of the same image taken with the same microscope, an image from a different tissue sample with a different microscope, and an image taken with a confocal microscope. We observed good, medium and poor results, respectively, illustrating that performance depends on the similarity between test and training data. In subsequent tests using data augmentation, we achieved sensitivity of 0.82±0.07 and specificity of 0.87±0.16 on N = 6 sample sections from 3 different BCCs imaged with the same microscope system. These data show that the U-Net performed well with a relatively few number of training images. Examining the errors raised interesting questions regarding what the errors mean and how they possibly arose. By creating a surgeon interface for rapid pathological assessment and machine learning diagnostics for pathological features, the BCC diagnosis process will be expedited and standardized.