Towards Skin Cancer Self-Monitoring through an Optimized MobileNet with Coordinate Attention

Abstract

Skin cancer is one of the most frequent type of cancer, which is tipically divided in two types: melanoma and non-melanoma. Melanoma is the least common, but also the deadliest of them if left untreated in early stages. Thus, skin cancer monitoring is key for early detection, which could be done with the help of mobile devices and artificial intelligence solutions. In this sense, local deployment is suggested to embrace simplicity and avoid data privacy and security issues. However, current high-performance neural networks are extremely challenging to be deployed in mobile devices due to resource constraint, so lighter but effective models are required to make local deployment possible. In this work, simplifying an already light model, such as MobileNetV2, is pursued, combining it with an attention mechanism to enhance the network's capability to learn and compensate for the lack of information that simplifying the original architecture might cause. Fine-tuning was applied, using an autoencoder to pre-train the model on the CIFAR100 dataset. Experiments covering four scenarios were carried out using HAM10000 dataset. Promising results were obtained, reaching the best performance using a simplified MobileNetV2 combined with Coordinate Attention mechanism with less than a million parameters in total and up to a 83.93 % of accuracy.