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.

Summary

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.


Keywords
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
  • 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
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  • 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
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  • Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images
  • Abstract Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase, COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and… More
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  • 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
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  • 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:171       Downloads:159        Download PDF

  • Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning
  • Abstract The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain… More
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories
  • Abstract The COVID-19 disease has already spread to more than 213 countries and territories with infected (confirmed) cases of more than 27 million people throughout the world so far, while the numbers keep increasing. In India, this deadly disease was first detected on January 30, 2020, in a student of Kerala who returned from Wuhan. Because of India’s high population density, different cultures, and diversity, it is a good idea to have a separate analysis of each state. Hence, this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of… More
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  • 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:345       Downloads:227        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:412       Downloads:287        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:774       Downloads:336        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:390       Downloads:271        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
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  • 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
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