Secure Medical Imaging Data Using Cryptography with Classification

Abstract

Medical imaging data in today's healthcare information systems is an essential part of diagnostics. The secure medical imaging data plays a critical role in current time but today it is complex task of maintaining data privacy so the main objective of this study to solve this problem. In this project firstly we secure the MRI images of the brain using cryptography. In this process input images are encrypted & decrypted using public key cryptography and supplied as an input to the pre-trained convolutional neural network such as Alex-net. The model comprises of the 25 layers such as convolutional, batch-normalization, ReLU and max-pooling etc. The classification between the tumor and healthy images has been performed using SoftMax layer. The performance of the proposed model has been tested on publically available BRATS-2020 Challenging dataset. The proposed model achieved up to the 97% prediction scores that are far better as compared to the latest published research work in this domain.