Special Issue "AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems"

Submission Deadline: 30 December 2020 (closed)
Guest Editors
Dr. Mazin Abed Mohammed, University of Anbar, Iraq.
Dr. Mohd Khanapi Abd Ghani, Universiti Teknikal Malaysia Melaka, Malaysia.
Dr. Mashael S. Maashi, King Saud University, Saudi Arabia.
Dr. Jon Arambarri, ESTIA, France.


Artificial intelligence (AI) and its applications are now the hottest research areas. In recent years, there have been more and more AI applications in the medical field. AI technology is promoting the development of the medical and health industries. In the medical domain, AI techniques can be used to develop clinical decision support systems to help with medical diagnostics. AI technologies can be also deployed in various medical devices, trackers, and information systems. A huge amount of patient data is recorded in the electronic medical record (EMR) database, including diagnosis, medical history, medications, and lab results. Through the process of extraction, transformation, and loading (ETL), researchers can generate a patient dataset worthy of analysis by AI techniques. In addition to the data analysis using structure data, AI techniques are now used for medical image recognition, medical text, and semantic recognition, and molecular biological testing. The analysis results can be used as a reference for the evaluation of patients by the medical team. Recently, AI, internet-of-things (IoT), big data analytics, machine learning, deep learning, Fog Computing, cloud computing and block chain technologies have been intelligently applied with various applications in networking, Medical diagnosis and Healthcare Systems, shipping to build efficient, sustainable systems and Intelligent Solutions to Medical and Healthcare Systems.

This Special Issue focus on advanced techniques in signal processing, analysis, modelling, and classification, applied to a variety of medical diagnostic problems. Biomedical data play a fundamental role in many fields of research and clinical practice. Very often the complexity of these data and their large volume makes it necessary to develop advanced analysis techniques and systems. Furthermore, the introduction of new techniques and methodologies for diagnostic purposes, especially in the field of medical imaging, requires new signal processing and machine learning methods. The recent progress in machine learning techniques, and in particular deep learning, has revolutionized various fields of artificial vision, significantly pushing the state of the art of artificial vision systems into a wide range of high-level tasks. Such progress can help address problems in the analysis of biomedical data.

This Special Issue seeks original, high-quality contributions that investigate AI applications in healthcare. The main topics of interest include but are not limited to the following:
• AI and big data analytics applied in medical domain;
• AI methodologies for medical data analysis;
• Administrative data analysis using AI techniques;
• Intelligent medical efficient solutions for future applications;
• AI and block chain assisted medical efficient product designs;
• Optimization of medical assets using machine learning and deep learning techniques;
• Smart IoT sensor design and optimal utilization in Healthcare Systems;
• Applications of artificial intelligence, block chain IoT for sustainable medical and service;
• AI based intelligent solutions for Healthcare Systems;
• Machine learning applied to Healthcare Systems;
• AI solutions to intelligent transportation systems;
• Medical data acquisition, cleaning and integration using AI methodologies;
• Medical image recognition using AI technologies;
• Natural language processing in medical documents;
• Computer-aided diagnosis;
• Artificial neural networks;
• Machine learning;
• Deep learning;
• COVID-19 Epidemiology • Machine and deep learning approaches based observation in case of COVID-19;
• Computational correlation in pneumonia and COVID-19;
• Computational methods for COVID-19 prediction and detection;
• Data mining and knowledge discovery in healthcare;
• Decision support systems for healthcare and wellbeing;
• Optimization for symptoms detection;
• Medical expert systems;
• Applications of artificial intelligence techniques in in case of COVID-19;
• Intelligent computing and platforms;
• Big data frameworks and architectures for applied computation;
• Visualization and interactive interfaces in case of COVID-19;
• Role of machine learning and computational methods in mental stress observations due to lockdown;
• COVID-19 analysis using Big Data;
• COVID-19 analysis using pattern recognition;
• Medical imaging using computer vision for COVID-19;
• Information Technology participation in Patient monitoring and tracking for COVID-19;
• Medical Management system for COVID-19;
• Treatment simulation model and analysis for COVID-19;
• Telemedicine system for COVID-19;
• Big Data Analytics for prediction and application for COVID-19;
• Big data analytics for prediction in medicine and health related applications;
• Medical Pattern recognition;
• Medical Image reconstruction;
• Multi-modality fusion;
• Statistical Medical pattern recognition;
• Medical Segmentation;
• Medical Image fusion;
• Medical Image retrieval. biological imaging Molecular/pathologic image analysis gene data analysis multiple modalities X-ray CT MRI PET ultrasound;

Published Papers
  • DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection
  • Abstract With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore, it is important to design… More
  •   Views:1078       Downloads:820       Cited by:3        Download PDF

  • Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources
  • Abstract Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized… More
  •   Views:1251       Downloads:992        Download PDF

  • 3D Semantic Deep Learning Networks for Leukemia Detection
  • Abstract White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of… More
  •   Views:1255       Downloads:990       Cited by:4        Download PDF

  • Hybrid Trainable System for Writer Identification of Arabic Handwriting
  • Abstract Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles. Previous studies have indicated that a word’s attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient (HOG) features are modified to… More
  •   Views:1405       Downloads:927       Cited by:1        Download PDF

  • Data-Fusion for Epidemiological Analysis of Covid-19 Variants in UAE
  • Abstract Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain… More
  •   Views:2026       Downloads:1350        Download PDF

  • A New Hybrid Feature Selection Method Using T-test and Fitness Function
  • Abstract

    Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes the well-designed fitness function… More

  •   Views:1139       Downloads:920        Download PDF

  • A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques
  • Abstract The writer identification (WI) of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations. It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies, including old national and religious archives. In this study, we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on blocks. This modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis… More
  •   Views:1321       Downloads:1093        Download PDF

  • Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records
  • Abstract Background: Electronic Health Record (EHR) systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally. However, the existing EHR systems mostly lack in providing appropriate security, entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures. Objective: To solve this delicate problem, we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems. Methodology: In our EHR blockchain system, Peer nodes from various organizations (stakeholders) create a ledger network, where… More
  •   Views:2578       Downloads:1775       Cited by:4        Download PDF

  • Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification
  • Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused… More
  •   Views:1614       Downloads:1049       Cited by:1        Download PDF

  • A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction
  • Abstract Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest… More
  •   Views:1168       Downloads:1049       Cited by:1        Download PDF

  • Multiclass Stomach Diseases Classification Using Deep Learning Features Optimization
  • Abstract In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage. In this paper, an optimized deep learning method is presented for multiple stomach disease classification. The proposed method work in few important steps—preprocessing using the fusion of filtering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and finally fusion of optimal vectors and classification using Multi-Layered Perceptron Neural Network (MLNN). In the feature extraction step, pre-trained Inception V3 is utilized and retrained on… More
  •   Views:1399       Downloads:1385       Cited by:3        Download PDF

  • Exploiting Deep Learning Techniques for Colon Polyp Segmentation
  • Abstract As colon cancer is among the top causes of death, there is a growing interest in developing improved techniques for the early detection of colon polyps. Given the close relation between colon polyps and colon cancer, their detection helps avoid cancer cases. The increment in the availability of colorectal screening tests and the number of colonoscopies have increased the burden on the medical personnel. In this article, the application of deep learning techniques for the detection and segmentation of colon polyps in colonoscopies is presented. Four techniques were implemented and evaluated: Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC).… More
  •   Views:1690       Downloads:1010        Download PDF

  • Epidemiologic Evolution Platform Using Integrated Modeling and Geographic Information System
  • Abstract At the international level, a major effort is being made to optimize the flow of data and information for health systems management. The studies show that medical and economic efficiency is strongly influenced by the level of development and complexity of implementing an integrated system of epidemiological monitoring and modeling. The solution proposed and described in this paper is addressed to all public and private institutions involved in the fight against the COVID-19 pandemic, using recognized methods and standards in this field. The Green-Epidemio is a platform adaptable to the specific features of any public institution for disease management, based… More
  •   Views:1283       Downloads:885        Download PDF

  • Diabetes Type 2: Poincaré Data Preprocessing for Quantum Machine Learning
  • Abstract Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising. Albeit being largely studied, VQC implementations for “real-world” datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification problems using NISQ devices. By… More
  •   Views:1382       Downloads:971       Cited by:1        Download PDF

  • Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection
  • Abstract Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial… More
  •   Views:1352       Downloads:1014        Download PDF

  • Toward the Optimization of the Region-Based P300 Speller
  • Abstract Technology has tremendously contributed to improving communication and facilitating daily activities. Brain-Computer Interface (BCI) study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis (ALS). However, with the advancements in cost-effective electronics and computer interface equipment, the BCI study is flourishing, and the exploration of BCI applications for people without disabilities, to enhance normal functioning, is increasing. Particularly, the P300-based spellers are among the most promising applications of the BCI technology. In this context, the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem… More
  •   Views:1312       Downloads:891        Download PDF

  • Identification of Thoracic Diseases by Exploiting Deep Neural Networks
  • Abstract With the increasing demand for doctors in chest related diseases, there is a 15% performance gap every five years. If this gap is not filled with effective chest disease detection automation, the healthcare industry may face unfavorable consequences. There are only several studies that targeted X-ray images of cardiothoracic diseases. Most of the studies only targeted a single disease, which is inadequate. Although some related studies have provided an identification framework for all classes, the results are not encouraging due to a lack of data and imbalanced data issues. This research provides a significant contribution to Generative Adversarial Network (GAN)… More
  •   Views:1409       Downloads:907       Cited by:7        Download PDF

  • Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features
  • Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level… More
  •   Views:2107       Downloads:1060       Cited by:9        Download PDF

  • Metaheuristic Clustering Protocol for Healthcare Data Collection in Mobile Wireless Multimedia Sensor Networks
  • Abstract Nowadays, healthcare applications necessitate maximum volume of medical data to be fed to help the physicians, academicians, pathologists, doctors and other healthcare professionals. Advancements in the domain of Wireless Sensor Networks (WSN) and Multimedia Wireless Sensor Networks (MWSN) are tremendous. M-WMSN is an advanced form of conventional Wireless Sensor Networks (WSN) to networks that use multimedia devices. When compared with traditional WSN, the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content. Hence, clustering techniques are deployed to achieve low amount of energy utilization. The current research work aims at introducing a new… More
  •   Views:1402       Downloads:930       Cited by:2        Download PDF

  • IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification
  • Abstract Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can be viewed as a fusion… More
  •   Views:1955       Downloads:1193        Download PDF

  • IoT Technologies for Tackling COVID-19 in Malaysia and Worldwide: Challenges, Recommendations, and Proposed Framework
  • Abstract The Coronavirus (COVID-19) pandemic is considered as a global public health challenge. To contain this pandemic, different measures are being taken globally. The Internet of Things (IoT) has been represented as one of the most important schemes that has been considered to fight the spread of COVID-19 in the world, practically Malaysia. In fact, there are many sectors in Malaysia would be transformed into smart services by using IoT technologies, particularly energy, transportation, healthcare sectors. This manuscript presents a comprehensive review of the IoT technologies that are being used currently in Malaysia to accelerate the measures against COVID-19. These IoT… More
  •   Views:3727       Downloads:4199        Download PDF