# Advances in IoMT for Healthcare Systems
## 2 Review of Published Articles  
Here, we provided a brief overview of the published articles (or contributions). In Contribution 1 of this Special Issue, the authors proposed a biometric framework that involves human finger-knuckle print (FKP) and iris for the authentication of users. The authors used biometric fusion of multiple images including the outer surface image of FKP finger phalangeal joint and skin pattern image of the iris. The pattern matching of the obtained images is performed at a match score level, in which the neuro-fuzzy classifier is used for features extraction and anomaly detection. The accuracy is reported as greater than 98%, which is higher than that of single biometric classifiers. The use of multiple biometrics is attributed as a solid recognition system in terms of biometric fusion of inherent physical and/or behavioral characteristics compared to a single biometric such as the fingerprint, face, palm print, retina, voice, hand geometry, gait, etc. In Contribution 2, the authors employed the deep learning model of the convolutional neural network (CNN) for the initial stage detection and classification of Alzheimer's disease (AD), which is a type of brain disorder and dementia. Neuroimaging modalities are used to capture brain modifications through PET (positron emission tomography) and categorize the AD patterns in terms of normal, mild, and severe. The CNN architectures are deployed in both 2D and 3D domains, in which blurring filters have been used before the downside subsampling of the images. The 2D and 3D CNN architecture outperformed the existing classification methods in terms of accuracy. The use of modern ML models not only helps diagnose and classify AD, but can also be used for the early detection of other fatal diseases. In Contribution 3, the authors used AI and hybrid deep learning algorithms for optimized fetal health records in cardiotocography (CTG), which is a composite diagnostic test of the fetal uterine activity and heart rate. In this research, a deep transfer learning model is proposed to reduce computational time and optimize classification accuracy in CTG records. The proposed model achieved better results compared to the related models (including: GoogleNet, DenseNet, RCNNs, and ResNet) in a real-time clinical environment. In modern clinical practices, good classification specificity and accuracy rate of CTG datasets can enable informed decision-making and a timely referral of chronic complications during delivery. In Contribution 4, the authors focused on the implementation of blockchain technology with novel architecture to protect healthcare data in cyberspace. In this architecture, the network members are distributed into decentralized clusters while a single copy of the blockchain is preserved for each cluster. The proposed architecture offers privacy protection and data security against cyberattacks with low computational and communication overhead compared to bitcoin and lightweight blockchain networks. The design of novel blockchain data security algorithms is more crucial for patients' trust because cyber-attacks are becoming more diverse with the growth of network nodes. In this regard, the proposed architecture could be a good candidate solution because it can reduce network traffic and improve ledger updates with the increase in the number of nodes. Contribution 5 discusses the implementation of deep learning models for object detection and face tracking in an automatic invigilation system. The training and testing accuracies of the suggested models are reported as good. The proposed invigilation model can equally be used for the provision of smart healthcare services such as remote patient monitoring and telemedicine. In Contribution 6, the authors present the analysis of network slicing (NS) regarding the increasing commercial needs and developing network challenges of fifth-generation (5G) technology.