Research Article
Security and Privacy of Cloud- and IoT-Based Medical Image Diagnosis Using Fuzzy Convolutional Neural Network
Algorithm 1
Medical image encryption and decryption.
| Input: image of size m × n, number of hidden layers (L), number of hidden nodes (h), compression rate (C) | | For n = 1: number of pixels | | Partition the image | | H = decompose the input image | | End | | Normalise all the subimage block matrices | | NET = create backpropagation neural network (L, h, C) | | Get compressed data | | Scrambled image = extended zigzag algorithm (input image, NET) | | Generate chaotic sequences. | | S1, S2 = chaotic random sequences | | Diffuse pixel values by xor algorithm | | C1 = encrypted image | | //Decryption | | Input = C1 | | S1, S2 = chaotic random sequences | | Diffuse pixel matrices | | Inverse zigzag algorithm | | Recovery of pixel blocks | | I = decrypted image |
|