Information Security and Artificial Intelligence–Assisted Diagnosis in an Internet of Medical Thing System (IoMTS) - Pi-Yun Chen; Yu-Cheng Cheng; Zi-Heng Zhong; Feng-Zhou Zhang; Neng-Sheng Pai; Chien-Ming Li; Chia-Hung Lin (IEEE Access)
The internet of medical thing system (IoMTS) comprises the fifth-generation (5G) networking technology that collects and shares digital data from signal- or image-capturing devices through computer and wireless communication networks. This framework enables healthcare professionals to gain immediate visibility into a patient’s condition and facilitates communication with patients and family members. Recently, artificial intelligence (AI)- based methods are being increasingly applied to preprocess digital data and extract features. The key physiological parameters and feature patterns can then be incorporated into AI- based tools to help monitor, detect, and diagnose applications. However, these digital data contain patients’ privacy and may be restricted to authorized users. In a public channel, IoMTS must ensure information security for protection against hacker attacks. Hence, in this study, a symmetric encryption and decryption protocol was designed to ensure infosecurity of biosignals and medical images and assist in specific purposes in disease diagnosis. For a symmetric cryptography scheme, this study proposed a key generator combining a chaotic map and Bell inequality and generating unordered numbers and unrepeated 256 secret keys in the key space. Then, a machine learning - based model was employed to train the encryptor and decryptor for both biosignals and image infosecurity. After secure - data transmission, a case study is conducted for classifying medical images. Here, a classifier based on a convolutional neural network (CNN) is used for AI- assisted breast tumor diagnosis. In addition, for biosignal infosecurity, raw data were collected from a radar millimeter-wave (mm-Wave) sensing firmware for detecting vital signs. The experimental results are validated for heartbeat signals, respiratory signals, and mammography images, demonstrating the effectiveness and feasibility of the proposed encryption, decryption, and AI-assisted diagnosis methods.