A Novel Approach of Smart Contract based Distributed Ledger Technology using Deep Learning Techniques to Secure Medical Images

Internet of Medical Things (IoMT) is on-demand research area, generally utilized in most of medical applications. Security is a challenging problem in decentralized platform while handling with medical data or images. An effective deep learning-based blockchain framework with reduced transaction cost is proposed to enhance the security of medical images in IoMT. The proposed study involves four different stages like image acquisition, encryption, optimal key generation, secured storing. The input images initially are collected in the image acquisition stage. Then, the collected medical images are encrypted using coupled map lattice (CML). This encryption process assists to preserve the input medical images from the attackers. In order to provide more confidentiality to the encrypted images, optimal keys are generated using opposition-based sparrow search optimization (O-SSO) algorithm. These encrypted images are stored using distributed ledger technology (DLT) and smart contract based blockchain technology. This blockchain technology enhances the data integrity and authenticity and allows secured transmission of medical images. After decrypting the image, the disease is diagnosed in the classification stage using proposed Recurrent Generative Neural Network (RGNN) model. The proposed study used python tool for simulation analysis and the medical images are gathered from CT images in COVID-19 dataset. © 2022 IEEE.

Modified Ebola based image optimization algorithm for secure image transmission

Steganographic technique is a way of hiding confidential data that needs to be protected from unauthorized users and also no suspicion should be there that the cover image is carrying the secret information. Therefore, in steganography technique it is critical to have an algorithm that can hide information securely and uses best combination of techniques. In this research the 2 main factors are considered i.e., payload capacity and stego-image quality. In this model different techniques have been hybridised and those are DWT, Huffman Encoding and Ebola optimisation technique. These combinational techniques work on both capacity and image quality. In this database of Covid-19 patients is used as the confidential data and the results that this model has performed better than the existing ones. © 2022 IEEE.