Skin cancer is a possible curable disease when detected in the early stage, but skin cancer diagnosis is difficult for people in developing countries where residents lack access to proper healthcare. In this paper, we present a low-cost, easy-to-use, and internet-free prescreening solution to detect cancer earlier in rural areas where medical resources are scarce. We deliver a prototype of a device that can classify the skin anomaly into seven major categories and calculate the precise area of skin anomaly. The prototype we designed includes a Raspberry Pi 3B+, Pi camera, magnifying camera attachment, a convolution neural network powering skin cancer recognition, another network for skin cancer boundary segmentation, and an interactive user interface on a touchscreen in a custom enclosure. We trained a MobileNet v2 for skin cancer recognition and a U-Net for skin cancer boundary segmentation on the Skin Cancer MNIST dataset collected by The International Skin Imaging Collaboration and used it as our skin cancer recognition model. We then deployed both models onto a Raspberry Pi, and made it into a handy device that takes one close-up picture and prescreen the patient’s skin quickly.