Advancements in Communication Systems: From Image Compression Techniques to Deep Learning Applications

Abstract

With the advancement of medical technologies, it is now crucial to diagnose illnesses using medical imaging. Medical pictures frequently travel from one end of the network to the other via its branches. Thus, a high degree of security is needed. Unauthorised usage of the image's data causes issues. The significant expansion of the Internet of Things (IoT) in healthcare sector has raised serious concerns about security and integrity of medical data for applications that provide healthcare services. The aim of this research is to transmit the secure compressed medical image using communication system utilizing DL model. Input image has been compressed using convolutional equalizing quantizer with Gaussian scale encoder mixture model. then the secure image has been transmitted using Elliptic Curve wavelet transform based on cloud IoT model. the experimental analysis has been carried out for various input medical image compression and transmission in terms of data throughput, end-end delay, QoS, training accuracy, average precision. The proposed technique attained End-end delay of 61%, Data Throughput of 96%, QOS of 93%, training accuracy of 97%, average precision of 94%.