Secure Transmission of Medical Image Using a Wavelet Interval Type-2 TSK Fuzzy Brain-Imitated Neural Network

Abstract

Abstract The purpose of this research is to develop a new design of a wavelet interval type-2 takagi-sugeno-kang fuzzy brain-imitated neural network (WIT2TFBINN), which is a combination of the mathematical models of a Takagi-Sugeno-Kang (TSK) fuzzy system based on wavelet interval type-2 function (WIT2) and a wavelet interval type-2 fuzzy brain imitated neural network (FBINN). The proposed WIT2TFBINN is used for synchronization control of a 4D Lorentz chaotic system and has the benefits of wavelet interval type-2 membership function, TSK fuzzy inference system, decision making, and emotional activity. To provide fast training, the proposed method's parameter update laws are derived using the gradient descent method. The proposed WIT2TFBINN synchronization technique is then applied to the transmission of medical images in a secure manner. As a cipher image, a medical image is encrypted into a chaotic trajectory. After transmission, the image can be decrypted using chaotic trajectory synchronization on the received signal. By comparing the root mean square error and statistical test results of the proposed method with recent methods, the superiority of the proposed method is demonstrated.