Deep Learning and Blockchain Applications in Healthcare Sector Using Imaging Data

The healthcare industry has witnessed the emergence of deep learning (DL) along with blockchain technologies as potent instruments with considerable potential for transforming the sector, in the domain of imaging analysis of information. The healthcare industry is highly significant to society as it is responsible for preserving and enhancing human health. It includes preventive medicine, diagnostics, rehabilitation, therapy, and palliative care offerings. In recent years, there have been notable advancements and improvements in the medical field regarding using images by implementing DL and blockchain applications. Integrating DL and blockchain technology in healthcare imaging has presented novel prospects for cutting-edge research and advancement. Scholars are investigating using DL models to examine voluminous imaging datasets, thereby revealing valuable insights and patterns that can facilitate progress in healthcare knowledge and treatment methodologies. The adoption of blockchain technology in clinical trials contributes to promoting transparency and consistency. This is owing to the inherent advantages of blockchain, which enable the creation of a transparent and visible system. As a result, the integrity of data collected for research is being protected and trust in the outcomes of clinical trials is being fostered. Integrating DL and the blockchain system presents an intriguing chance to transform the field of telemedicine by facilitating the safe and confidential transfer and retention of medical images, thereby enabling remote diagnosis and advice. This chapter aims to showcase the applications of DL along with the blockchain in healthcare using an imaging dataset. Using a colossal collection of data, combined with deep learning and blockchain techniques, it can be trained to exhibit the desired behaviour. Applications of DL and blockchain technology with imaging dataset for various diseases such as cancer, diabetic retinopathy, Alzheimer's etc. can aid medical professionals in the early investigation and classification of diseases so that stricken are given effective therapies.