Medical images can be constructed in two-dimensional (2D) or three-dimensional (3D) view imaging to be applied in disease detection and diagnosis inside the body, such as cancer/tumor, heart, or lung-related diseases. These images may contain patients’ privacy information and clinical diagnosis records. Hence, these images need to ensure authorization demands among hospitals, medical service organizations, or physicians in a picture archiving and communication system. This study presents an intelligent symmetric cryptography with a chaotic map and quantum-based key generator (KG) for medical image encryption and decryption. Overall scheme processes include (1) random cipher code generation, (2) training gray relational analysis (GRA)-based encryptor and decryptor, and (3) decrypted image evaluation. The hybrid chaotic map and quantum-based KG are used to increase the chaotic complexity and unpredictable levels to produce cipher codes for changing pixel values (substitution method) in a 2D image by 256 key-space cipher codes. The first and second GRA models are used to train the cipher codes to achieve an encryptor and a decryptor, respectively. Through the methodology validation using a chest X-ray database, the structural similarity index measurement is employed to evaluate the decryption quality between the plain image and decrypted image. The encrypted images show a visual uncorrelation with the plain images, and experimental results indicate higher confidences against the passive eavesdropper.

Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates are used to extract polar expression features such as the key parameters deviation (cm) and accumulation angle (rad). These parameters are quantitative manner to scale the variations of functional tremors in normal control subjects and patients with Parkinson’s disease (PD) and essential tremor (ET). However, difficulty arises in using nonlinear polar expression features in the two-dimensional feature space to separate normal control subjects from those with PD and ET. To solve the nonlinear separable classification problem, hash transformation is used to map polar expression features to a high-dimensional space using hash weighing function and modulo operation. Then, a machine learning method, such as the generalized regression neural network (GRNN), is implemented to train a decision-making classifier using the particle swarm optimization (PSO) algorithm for possible class assessment. With the enrolled data from 50 subjects, the fivefold cross validation, mean true positive, mean true negative, and mean hit rates of 98.93%, 98.96%, and 98.93%, respectively, are obtained to quantify the performance of the proposed decision-making classifier to identify normal controls and subjects with PD or ET. The experimental results indicate that the proposed screening model can improve the accuracy rate compared with the conventional machine learning classifier.

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.

Lung diseases can result in acute breathing problems and prevent the human body from acquiring enough oxygen. These diseases, such as pneumonia (P), pleural effusion (Ef), lung cancer, pneumothorax (Pt), pulmonary fibrosis (F), infiltration (In) and emphysema (E), adversely affect airways, alveoli, blood vessels, pleura and other parts of the respiratory system. The death rates of P and lung cancer are higher than those of other typical lung diseases. In visualization examination, chest radiography, such as anterior-posterior or lateral image viewing, is a straightforward approach used by clinicians/radiologists to diagnose and locate possible lung abnormalities rapidly. However, a chest X-ray image of patients may show multiple abnormalities associated with coexisting conditions, such as P, E, F, Pt, atelectasis, lung cancer or surgical interventions, which further complicate diagnosis. In addition, poor-quality X-ray images and manual inspection have limitations in digital image-automated classification. Hence, this study intends to propose a multilayer machine vision classifier to automatically identify the possible class of lung diseases within a bounding region of interest (ROI) on a chest X-ray image. For digital image texture analysis, a two-dimensional (2D) fractional-order convolution (FOC) operation with a fractional-order parameter, $v =0.3-0.5$ , is used to enhance the symptomatic feature and remove unwanted noises. Then, maximum pooling is performed to reduce the dimensions of feature patterns and accelerate complex computations. A multilayer machine vision classifier with radial Bayesian network and gray relational analysis is used to screen subjects with typical lung diseases. Anterior-posterior chest X-ray images from the NIH chest X-ray database (NIH Clinical Center) are enrolled. For digital chest X-ray images, with $K$ -fold cross-validation, the proposed multilayer machine vision classifier is applied to facilitate the diagnosis of typical lung diseases on specific bounding ROIs, as promising results with mean recall (%), mean precision (%), mean accuracy (%) and mean F1 score of 98.68%, 82.42%, 83.57% and 0.8981, respectively, for assessing the performance of proposed multilayer classifier for rapidly screening lung lesions on digital chest X-ray images.

To efficiently detect non‐technical loss and power blackout in micro‐distribution systems, this study proposes using a cooperative game (CG) based inference mechanism under the advanced metering infrastructure technique. Fractional‐order Sprott system is designed to extract specific features between the profiled usages and the measurement usages in real time analysis. The fractional‐order dynamic errors are positive correlated with the changes in load usages, including normal conditions, electricity fraudulent events, and power blackout events. Then, multiple agents in a game and multiple CG based inference mechanisms are used to locate abnormalities in micro‐distribution systems. For energy management applications, the proposed inference mechanism can identify the 2.5–20% irregular usages during normal demand operations. In addition, it can also identify the large changes >20% in usages, while a micro‐distribution system is disconnected to operate in the islanded mode within a few hours. This function can address an outage occurrence and then quickly resume service using the service restoration strategy and distributed generations in a local grid. Using a medium‐scale micro‐distribution system, computer simulations are conducted to show the effectiveness of the proposed inference model.

Digital physiological signals in telecare medicine information systems have been widely applied in remote medical applications, such as telecare, tele-examination, and telediagnosis, via computer networking transmission or wireless communication. However, these medical records need to ensure authorization demands in the channel model for human body communication and remote medical servers and enhance the confidentiality, recoverability, and availability of transmission data. Hence, this study proposes a symmetric cryptography scheme with a chaotic map and a multilayer machine learning network (MMLN) to achieve physiological signal infosecurity. A chaotic pseudorandom number generator within specific control parameters can dynamically produce unordered sequence numbers to set the secret keys for a regular secret key update, thereby improving the security of private cipher codes. The chaotic map is quickly iterated to produce a pseudorandom key stream for real-time applications, and the private cipher codes are selected using the initial and specific control parameters at the data emitter and receiver ends. A general regression neural network is used to map the high-dimensional input-output pair of cipher codes for substitution and permutation processes. Its adaptive MMLN with an optimization algorithm can rapidly train the random cipher code protocol to achieve an encryptor and a decryptor for a regular encrypted communication. Using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database, 100 electrocardiogram fragments are used to verify the proposed model, and the peak signal-to-noise ratio (PSNR) as a quantitative quality metric is used to evaluate the visual quality after encryption and decryption processes for further diagnosis applications. Experimental results show that the proposed scheme has a higher mean PSNR (35.26 ± 3.77 dB) and shorter mean executing time (0.16 ± 0.01 s) compared with traditional cryptography protocol schemes.