Development of Mobile Skin Cancer Detection using Faster R-CNN and MobileNet v2 Model

The development of cameras in smartphones is possible as a point of care for early detection of cancer. Early detection using smartphones is carried out by giving the smartphone the ability to recognize objects with skin cancer characteristics. The convolution neural network (CNN) is often used in disease detection and classification. However, the CNN method requires high computing capability and a large memory that is difficult to perform on smartphones. In this paper, the MobileNet v2 and Faster R-CNN methods are utilized and executed on an Android-based application that can detect skin cancer. Both proposed architectures were trained to recognize actinic keratosis and melanoma skin cancer targets on images. The dataset used was 600 images, divided into two classes, actinic keratosis images and melanoma images with no attention for gender, age, or additional factors. In this study, an android app was developed to utilize the smartphone camera for skin cancer detection. The Faster R-CNN and MobileNet v2 models were implemented as an intelligent system for the screening. Two testing methods were performed in this study, the Jupyter notebook and android camera. Based on the experiment result, Faster R-CNN obtained higher accuracy when testing using the Jupyter, and MobileNet v2 got the same high accuracy when applying on a smartphone.