Enhancing healthcare cloud security with intelligent shuffled frog leaping optimized sequential long short-term memory (ISFLO-SLSTM)

Abstract

Although medical image processing consumes much memory, computing time and specialized platforms are needed. As a result, it is crucial to implement cost-effective solutions to replace outdated ones. Due to these factors, we choose to adopt cloud computing to satisfy our needs for large-scale data processing. Meanwhile, this strategy offers accessibility to services upon request with excellent reliability and versatility. Therefore, employing cloud services rather than internal applications will assist healthcare organizations in outsourcing calculations to a third party, thereby reducing operating costs. However, to avoid malicious data leaks, comprehensive protection of information across unauthorized users and untrustworthy clouds is necessary. Different frameworks are created to allow consumers to save and analyze their information utilizing cloud-based computing. They are developed utilizing distributed systems, cryptosystems, or an amalgam of the two. The primary issue with applying cryptography methods for the enormous analysis of information on the cloud is the computing cost of image processing operations. Preventing unauthorized usage of healthcare records and private health details is the main concern. To secure data processing in a cloud environment by classifying image pixels, we offer a revolutionary intelligent shuffled frog jumping optimized sequential long short-term memory (ISFLO-SLSTM) approach. We added an additional layer, the cloud security module, to lower the danger of future medical data leakage. The effectiveness of the suggested strategy is assessed using medical RGB images taken from the Kaggle database. The findings of this study show how the ISFLO-SLSTM system has the potential to revolutionize healthcare information analysis by protecting patient privacy and security that utilizes the full extent of the latest machine learning methods.