Blockchain Enabled Secure Medical Data Transmission and Diagnosis Using Golden Jackal Optimization Algorithm with Deep Learning

Abstract

Abstract The incorporation of deep learning (DL) and blockchain (BC) technologies in healthcare revolutionizes disease diagnoses and improves data security. The tamper-resistant and decentralized nature of BC safeguards the integrity and confidentiality of medical records, alleviating the risk of any unauthorized access. At the same time, DL techniques leverage intricate patterns within healthcare information to enhance the accuracy and speed of disease diagnoses. With this motivation, this article proposes a new BC with a golden jackal optimization algorithm enabled DL assisted secure medical data transmission and diagnoses (BGJOA-DLSMTD). The objective of the BGJOA-DLSMTD algorithm is to analyze the disease with a high detection rate and securely transfer the medical images. The BGJOA-DLSMTD algorithm integrates various levels of operations namely encryption, image acquisition, BC, and diagnostic process. Initially, GJOA with a homomorphism encryption system is employed for the process of image encryption wherein the optimum keys could be produced by the GJOA technique. Also, BC is implemented to store the encrypted imageries. Next, the diagnostic method includes Bayesian optimization algorithm (BOA) based hyper parameter tuning, deep belief network (DBN)-based classification, and CapsNet-based feature extraction. The empirical analysis of the proposed BGJOA-DLSMTD algorithm has been demonstrated by means of standard medical images and the outcomes underlined the superior achievement of the BGJOA-DLSMTD algorithm.

Keywords

• Blockchain
• Medical Data
• Internet of Things
• Homomorphic Encryption
• Golden Jackal Optimization
• Hyper parameter Tuning