A collective review of Terahertz technology integrated with a newly proposed split learningbased algorithm for healthcare system

Abstract

The Now a days, Terahertz (THz) imaging can be used to improve healthcare in several ways. Firstly, THz imaging can be used for cancer detection, allowing for early detection before visible symptoms appear. THz imaging can distinguish between healthy tissue and cancerous tissue, providing sharper imaging and molecular fingerprinting. Furthermore, THz imaging can be integrated with artificial intelligence, internet of things, cloud computing, and big data analytics to create more sophisticated healthcare systems. Split learning is a privacypreserving method that collectively with deep learning method used in the healthcare system to train collaborative models without sharing raw patient data between clients. Split learning algorithms, occur when training models sequentially, making them more robust and effective. Multi-site split learning is a novel algorithm that enables secure data transfer between hospitals, ensuring privacy while achieving optimal performance. To full fill this objective here we have review various THz technology on the healthcare system and introduced a machine learning with a split learning based secured method, that has the potential to revolutionize healthcare by enabling early detection, improving diagnostics, and facilitating personalized treatment approaches. The article also associate with various review algorithm’s potential impact on compact and portable consumer devices, such as smartphones and wearable health trackers, which may applicable on real life.