POMIC: Privacy-Preserving Outsourcing Medical Image Classification Based on Convolutional Neural Network to Cloud

Abstract

In the medical field, with the increasing number of medical images, medical image classification has become a hot spot. The convolutional neural network, a technology that can process more images and extract more accurate features with nonlinear models, has been widely used in this field. However, the classification process with model training with existing medical images needs a large number of samples, and the operation involves complex parameter computations, which puts forward higher requirements for users. Therefore, we propose a scheme for flexible privacy-preserving outsourcing medical image classification based on a convolutional neural network to the cloud. In this paper, three servers on the cloud platform can train the model with images from users, but they cannot obtain complete information on model parameters and user input. In practice, the scheme can not only reduce the computation and storage burdens on the user side but also ensure the security and efficiency of the system, which can be confirmed through the implementation of the experiment.

Keywords

• cloud computing
• secure outsourcing
• privacy preservation
• convolutional neural network
• medical image classification