Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.

*Published in the IEEE Electronics Packaging Society Section within IEEE Access.

View this article on IEEE Xplore

 

Multi-Energy Computed Tomography and its Applications

Submission Deadline:  01 May 2021

IEEE Access invites manuscript submissions in the area of Multi-Energy Computed Tomography and its Applications.

X-ray Computed Tomography (CT) can reconstruct the internal image of an object by passing x-rays through it and measuring the information. However, the conventional CT not only has poor performance in tissue contrast and spatial resolution, but also fails to provide quantitative analysis results and specific material components. To avoid these limitations, as a natural extension of the well-known dual-energy CT, the multi-energy CT (MECT) has emerged and is attracting increasing attention. A typical MECT system has great potential in reducing x-ray radiation doses, improving spatial resolution, enhancing material discrimination ability and providing quantitative results by collecting several projections from different energy windows (e.g. photon-counting detector technique) or spectra (e.g. fast kV-switching technique) either sequentially or simultaneously. It is a great achievement in terms of tissue characterization, lesion detection and material decomposition, etc. This can enhance the capabilities of imaging internal structures for accurate diagnosis and optimized treatments.

On the one hand, the limited photons within the narrow energy windows can result in energy response inconsistency. On the other hand, due to spectral distortions (e.g., charge sharing, K-escape, fluorescence x-ray emission and pulse pileups), the projections of MECT are tarnished by complicated noise. In this case, it is a challenge to find meaningful insights by utilizing these projections for practical applications. Therefore, there are new research opportunities to overcome this issue for higher levels of MECT imaging and applications.

This Special Section on IEEE Access aims to capture the state-of-the-art advances in imaging techniques for MECT and other related research.

The topics of interest include, but are not limited to:

  • MECT image reconstruction
  • MECT image denoising
  • MECT material decomposition
  • MECT hardware development
  • MECT system design
  • MECT image analysis
  • MECT image quality assessment
  • Applications of machine learning in MECT
  • X-ray spectrum estimation for MECT
  • Clinical diagnosis using MECT technique
  • Multi-contrast contrast agent imaging
  • K-edge imaging technique
  • Simulation software package for MECT imaging
  • Scattering correction for MECT
  • Artifacts removal of MECT image
  • Noise estimation models for MECT imaging

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor:  Hengyong Yu, University of Massachusetts Lowell, USA

Guest Editors:

    1. Yuemin Zhu, CNRS, University of Lyon, France
    2. Raja Aamir Younis, Khalifa University of Science and Technology UAE

 

Relevant IEEE Access Special Sections:

  1. Deep Learning Algorithms for Internet of Medical Things
  2. Millimeter-Wave and Terahertz Propagation, Channel Modeling and Applications
  3. Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies and Applications

 

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: Hengyong-yu@ieee.org.

Emerging Deep Learning Theories and Methods for Biomedical Engineering

Submission Deadline: 31 August 2020

IEEE Access invites manuscript submissions in the area of Emerging Deep Learning Theories and Methods for Biomedical Engineering.

The accelerating power of deep learning in diagnosing disease and analyzing medical data will empower physicians and speed up decision-making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated large amounts of biomedical information in recent years. However, new AI methods and computational models for efficient data processing, analysis, and modeling with the generated data is important for clinical applications and in understanding the underlying biological process.

Deep learning has rapidly developed in recent years, in terms of both methodological development and practical applications. It provides computational models of multiple processing layers to learn and represent data with various levels of abstraction. It can implicitly capture intricate structures of large-scale data and is ideally suited to some of the hardware architectures that are currently available.

The purpose of this Special Section aims to provide a diverse, but complementary, set of contributions to demonstrate new theories, techniques, developments, and applications of Deep learning, and to solve emerging problems in biomedical engineering.

The ultimate goal of this Special Section is to promote research and development of deep learning for multimodal & multidimensional signals in biomedical engineering by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

The topics of interest include, but are not limited to:

  • Theoretical understanding of deep learning in biomedical engineering
  • Transfer learning and multi-task learning
  • Joint Semantic Segmentation, Object Detection and Scene Recognition on biomedical images
  • Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming
  • Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.
  • Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)
  • Optimization by deep neural networks, multi-dimensional deep learning
  • New model or new structure of convolutional neural network
  • Visualization and explainable deep neural network
  • Missing data imputation for multi-source biomedical data
  • Sparse screening, feature screening, feature merging, quality assessment for biomedical data

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor: Yu-Dong Zhang, University of Leicester, United Kingdom

Guest Editors:

    1.  Zhengchao Dong, Columbia University, USA
    2. Juan Manuel Gorriz, University of Granada, Spain
    3. Yizhang Jiang, Jiangnan University, China
    4. Ming Yang, Nanjing Medical University, China
    5. Shui-Hua Wang, Loughborough University, UK

 

Relevant IEEE Access Special Sections:

  1. Deep Learning Algorithms for Internet of Medical Things
  2. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts
  3. Data-Enabled Intelligence for Digital Health

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  yudongzhang@ieee.org.

Behavioral Biometrics for eHealth and Well-Being

Submission Deadline: 28 February 2021

IEEE Access invites manuscript submissions in the area of Behavioral Biometrics for eHealth and Well-Being.

Artificial Intelligence (AI) is changing the healthcare industry from many perspectives. A very challenging issue deals with the development of non-intrusive AI technologies that could be integrated into everyday activities, thus allowing continuous health state monitoring and enabling automatic warnings when a dangerous change is predicted. Behavioral biometrics play a crucial role within this challenge. Behavioral biometrics, such as speech, handwriting, gait, etc. can be used to quantify human physiology, pathophysiological mechanisms, and actions. The final acquired signal is a mixture of at least four components:

  • The physical one, which enables the user to make the action (e.g. mouth, lips, tongue, etc.);
  • The cognitive one, which deals with mental abilities (learning, thinking, reasoning, remembering, problem-solving, decision-making, and attention);
  • The learned one, which deals with culture, habits, personalization, etc.;
  • The contingent contour one, which deals with the acquisition device, the emotional state, the specific task to be performed, etc.

It is evident that disease at its early stage, as well as during its course, could affect one or more of these components. Behavioral biometrics in eHealth seek solutions to diagnose, assess, and monitor diseases that are measurable just when the patient performs an action. This action could be walking, talking, writing or typing on a touchscreen, and many more. Behavioral biometrics also deal with the way the human being responds to natural and social events around her/him and emotions. The adoption of non-intrusive behavioral biometrics techniques in the set of daily activities would be pervasive: the user would be asked to do what she/he already does normally. The output of these systems could be provided to doctors, thus helping them in a deep disease inspection. At the same time these technologies could be directly adopted by doctors. These aspects are extremely important for the development of Computer Aided Diagnosis (CAD) tools. Nevertheless, specific behavioral biometrics tasks and activities could be planned to support rehabilitation activities.

This Special Section in IEEE Access aims to attract original research articles that advance the state of the art in behavioral biometrics for e-health and well-being. The goal is that it provides an opportunity to gain a significantly better understanding of the field’s current developments and future direction.

The topics of interest include, but are not limited to:

  • Signal processing techniques
  • Pattern Recognition techniques
  • Computer Vision techniques
  • Artificial Intelligence techniques
  • Continuous learning and recognition
  • Acquisition tools, procedures and protocols
  • Biometrics data mining
  • Wearable and non-intrusive sensors
  • Brain signals analysis for disease and emotional states recognition
  • Eye movement analysis for disease recognition
  • Face analysis for disease and emotional state recognition
  • Gait analysis for disease and emotional state recognition
  • Handwriting analysis for disease and emotional state recognition
  • Keystroke dynamics for disease and emotional state recognition
  • Sleep analysis for disease and emotional state recognition
  • Speech analysis for disease and emotional state recognition
  • Biometric data and clinical data fusion
  • Multiple behavioral biometrics
  • Development of complete CAD systems
  • Real-time health alerts and long-term health trend analytics
  • Applications

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor:  Donato Impedovo, University of Bari Aldo Moro, Italy

Guest Editors:

    1. Thurmon Lockhart, Arizona State University, United States
    2. Jiri Mekyska, Brno University of Technology, Czech Republic
    3. Bijan Najafi, Baylor College of Medicine, United States
    4. Toshihisa Tanaka, Tokyo University of Agriculture and Technology, Japan

 

Relevant IEEE Access Special Sections:

  1. Data-Enabled Intelligence for Digital Health
  2. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts
  3. Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: donato.impedovo@uniba.it.

Energy Harvesting Technologies for Wearable and Implantable Devices

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Energy Harvesting Technologies for Wearable and Implantable Devices.

Implantable and wearable electronic devices can improve the quality of life as well as the life expectancy of many chronically ill patients, provided that certain biological signs can be accurately monitored. Thanks to advances in packaging and nanofabrication, it is now possible to embed various microelectronic and micromechanical sensors (such as gyroscopes, accelerometers and image sensors) into a small area on a flexible substrate and at a relatively low cost. Furthermore, these devices have been integrated with wireless communication technologies to enable the transmission of both signals and energy.  However, to ensure that these devices can truly improve a patient’s quality of life, new preventative, diagnostic and therapeutic devices that can provide hassle-free, long-term, continuous monitoring will need to be developed, which must rely on novel energy harvesting solutions that are non-obstructive to their wearer.  So far, research in the field has focussed on materials, new processing techniques and one-off devices. However, existing progress is not sufficient for future electronic devices to be useful in any new application and a great demand exists towards scaling up the research towards circuits and systems. A few interesting developments in this direction indicate that special attention should be given towards the design, simulation and modeling of energy harvesting techniques while keeping system integration and power management in mind.

The topics of interest include, but are not limited to:

  • Novel piezoelectric, thermoelectric and photovoltaic energy harvesting technologies that lead to enhanced efficiency and controllability under standard or varying working conditions
  • Novel control strategies for achieving maximum or optimum energy harvesting
  • Power management circuits for energy harvesters
  • Novel data driven techniques for optimizing and forecasting the amount of energy that can be harvested
  • Low-Power circuits and sensors
  • Flexible sensors, circuits and energy harvesters for wearables
  • Implantable electronics
  • Novel wireless power transfer and delivery techniques
  • Numerical and computational modeling techniques

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Hadi Heidari, University of Glasgow, UK

Guest Editors:

    1. Mehmet Ozturk, North Carolina State University, USA
    2. Rami Ghannam,University of Glasgow, UK
    3. Law Man Kay, University of Macau, China
    4. Hamideh Khanbareh, University of Bath, UK
    5.  Abdul Halim Miah, University of Florida, USA

 

Relevant IEEE Access Special Sections:

  1. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts
  2. Neural Engineering Informatics
  3. Wearable and Implantable Devices and Systems


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  hadi.heidari@glasgow.ac.uk.

Deep Learning Algorithms for Internet of Medical Things

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Deep Learning Algorithms for Internet of Medical Things.

Traditionally, devices used in medical industry predominantly rely on medical images and sensor data; this medical data is processed to study the patient’s health condition and information. Presently, the medical industry requires more innovative technologies to process the large volume of data and improve the quality of service in patient care, and needs an intelligent system to detect early symptoms of diseases in the beginning stage and provide appropriate treatment.

Internet of Medical Things (IoMT) and its recent advancements have included a new dimension towards enhancing the medical industry practices and realizing an intelligent system. In addition, the medical data of IoMT systems is constantly growing because of increasing peripherals introduced in patient care.

Conventionally, medical image processing and machine learning are used for any medical diagnosis, subsequent treatment and therapies. However, with increasing volume of data with increased dimensions and dynamics of medical data, machine learning takes a back seat over another powerful classification mechanism named deep learning. Deep learning can solve more complicated problems, unsolvable by machine learning, and produce highly accurate diagnoses. The medical industry is one of the biggest industries which implements deep learning algorithms. Deep learning can handle the large volume of medical data, including medical reports, patients’ records, and insurance records, helping medical experts to predict the necessary treatment. The scalability of deep learning which helps to process and manipulate this huge volume of data makes an indomitable paradigm for computer-aided diagnosis in medical informatics. The significance of deep learning is compounded by the ever-improving technological aspects towards acquiring precise and multidimensional IoMT data with an eye on improving the accuracy of diagnosis. Overall, incorporating deep learning into IoMT can provide radical innovations in medical image processing, disease diagnosing, medical big data analysis and pathbreaking medical applications.

This Special Section in IEEE Access provides a perfect platform to discuss the prospective developments and innovative ideas in the healthcare domain, with the inception of deep learning-based biomedical systems in IoMT.

The topics of interest include, but are not limited to:

  • Deep learning for energy management in IoMT devices
  • Deep learning algorithms for medical big data analysis
  • Advancements in deep learning algorithms in health informatics
  • Programmers perspective view on deep learning in IoMT
  • Cognitive deep learning for wearable medical devices
  • Deep learning for data analytics in body sensor networks
  • Monitoring wearable medical devices using deep learning
  • Cyber security and malware detection in IoMT using deep learning
  • Deep neural networks for diagnosing and identifying suspicious lesions in IoMT
  • Evidence-based treatment using IoMT
  • Fraud prevention in healthcare using IoMT based data analytics model
  • Telemedicine using IoMT
  • Patient-centric model using deep learning with IoMT
  • Abnormal identification in patient monitoring system using deep learning with IoMT
  • Advanced medical image processing using deep learning
  • Disease diagnosis using deep learning in IoMT
  • Prioritized medical data transmission in IoMT using deep learning
  • Deep Belief Neural network and IoMT in in Future Medical Industries
  • Deep learning-based privacy preserving and security methods for medical data transmission in IoMT
  • Deep learning algorithm for medical decision support systems in IoMT based big data

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Wei Wei, Xi’an University of Technology, China

Guest Editors:

  1. Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil
  2. Naveen Chilamkurti, La Trobe University, Australia
  3. Marcin Woźniak, Silesian University of Technology, Poland
  4. Wael Guibene, Amazon Lab126, USA

 

Relevant IEEE Access Special Sections:

  1. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things
  2. Data Mining for Internet of Things
  3. Deep Learning for Computer-aided Medical Diagnosis


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: weiwei@xaut.edu.cn; taneo@126.com.

Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

Submission Deadline:  31 August 2019

IEEE Access invites manuscript submissions in the area of Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts.

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, evidence-based medical decision support etc. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely-coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data, and nonlinearly convert the patterns to high level medical insights.

This Special Section in IEEE Access invites academic and industrial experts to make their contributions to smart health big data, empowered by biomedical sensing and computational intelligence technologies. Studies are expected to connect the human body, data, and applications, establish an end-to-end information flow, and convert big data to big impacts. Crucial technologies include wearable sensing, in-home sensing, personal health record establishment, biomedical signal processing, deep learning, big data mining, pattern recognition, and other related techniques. This Special Section will allow readers to identify advancements, challenges and new opportunities in cutting-edge smart health big data innovations.

The topics of interest include, but are not limited to:

  • Wearable sensing for big data: bio-potential sensing, behavioral sensing, optical sensing, ultrasonic sensing, flexible sensing, emerging wearable imaging, etc.
  • In-home sensing for big data: on-bed sensing, sleep quality sensing, activity sensing, mobility sensing, fall detection, rehabilitation monitoring, etc.
  • Personal health big data: cardiac monitoring, cardiopulmonary monitoring, brain function monitoring, mobility monitoring, life style monitoring, etc.
  • Signal quality enhancement in wearable big data sensing
  • Biomedical signal processing for smart health big data
  • Feature extraction and critical feature selection from smart health big data
  • Automatic feature mining using deep learning from smart health big data
  • Dimension reduction for effective learning from smart health big data
  • Time series, image and unstructured data fusion
  • Data mining from large databases for pattern and correlation finding
  • Knowledge discovery in smart health big data
  • Telemedicine, internet of medical things, mobile health, remote health monitoring
  • Precision medicine exploration based on smart health big data
  • Medical relevant insight learning from long-term health records
  • Real-time health alerts and long-term health trend analytics
  • Sleep quality monitoring and analytics with smart health big data
  • Human-computer interaction for rehabilitation and assisted living
  • Lifestyle changing empowered by digital health technologies
  • Smart health big data to empower clinical trials

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Qingxue Zhang, Indiana University-Purdue University, USA

Guest Editors:

  1. Vincenzo Piuri, University of Milan, Italy
  2. Edward A. Clancy, Worcester Polytechnic Institute, USA
  3. Dian Zhou, University of Texas at Dallas & Fudan University, USA & China
  4. Thomas Penzel, Charite University Hospital, Germany
  5. Walter Hu, University of Texas at Dallas & One-Chip Co., Ltd., USA & China
  6. Hui Zheng, Harvard University & Massachusetts General Hospital, USA

 

Relevant IEEE Access Special Sections:

  1. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
  2. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions
  3. Human-Centered Smart Systems and Technologies


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: qxzhang@iu.edu.

Neural Engineering Informatics

Submission Deadline: 31 July 2019

IEEE Access invites manuscript submissions in the area of Neural Engineering Informatics.

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, cognitive systems have been proposed as useful and effective frameworks for the modeling and understanding of brain activity patterns. They also enable direct communication pathways between the brain and external devices (brain computer/machine interfaces). However, most of the research so far has focused on lab-based applications in constrained scenarios, which cannot be extrapolated to realistic field contexts. Considering the decoding of brain activity, biomedical engineers provide excellent tools to overcome the challenge of learning from brain activity patterns that are very likely to be affected by non-stationary behaviors and high uncertainty. The application of health and neural engineering to learning and modeling has recently demonstrated its remarkable usefulness for coping with the effects of extremely noisy environments, as well as the variability and dynamicity of brain signals. Additionally, neurobiological studies have suggested that the behavior of neural cells exhibits functional patterns that resemble the properties of computational neuroscience to encode logical perception. This paves the way for developing new computational neuroscience techniques in medicine and healthcare that foster the capabilities for modeling and understanding brain function from a quantitative point of view, which is also the basis of this Special Section in IEEE Access.

The topics of interest include, but are not limited to:

  • Novel models and theoretical computational learning for the synthesis and analysis of neuroimaging data: EEG, MEG, fMRI /MRI, PET/SPECT, fNIRS, DOI, EROS, etc
  • Modeling and learning for the recognition of cognitive processes including medical informatics and public health informatics
  • Bioinformatics decoding of brain activity patterns and brain computer/machine interfaces (BCI/BMI)
  • Imaging Informatics that explain the structure and function of the human brain in patients
  • Neuro methods for big data neuroimaging analytics and neuro-informatics
  • Hardware architectures in health and neuro-engineering

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Zehong Cao, University of Technology Sydney, Australia

Guest Editors:

  1. Peng Xu, University of Electronic Science and Technology of China, China
  2. Zhiguo Zhang, Shenzhen University, China
  3. Gang Wang, Xi’an JiaoTong University, China
  4. Samu Taulu, University of Washington, USA
  5. Leandro Beltrachini, Cardiff University, UK

 

Relevant IEEE Access Special Sections:

  1. Healthcare Information Technology for the Extreme and Remote Environments
  2. Advanced Information Sensing and Learning Technologies for Data- centric Smart Health Applications
  3. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: zhcaonctu@gmail.com or Zehong.Cao@uts.edu.au.

Deep Learning for Computer-aided Medical Diagnosis

Submission Deadline: 01 March 2019

IEEE Access invites manuscript submissions in the area of Deep Learning for Computer-aided Medical Diagnosis.

With the growing popularity of neuroimaging scanners in hospitals and institutes, the tasks of radiologists are increasing. The manual interpretation suffers from inter- and intra-radiologist variance. In addition, emotion, fatigue, and other factors will influence the manual interpretation result.

Computer-aided medical diagnosis (CAMD) are procedures in medicine to assist radiologists and doctors in the interpretation of medical images, which may come from CT, X-ray, ultrasound, thermography, MRI, PET, SPECT, etc. In practical situations, CAMD can help radiologists interpret a medical image within seconds.

Conventional CAMD tools are built on top of handcrafted features. Recent progress on deep learning opens a new era that can automatically build features from the large amount of data. On the other hand, many important medical projects were launched during the last decade (Human brain project, Blue brain project, Brain Initiative, etc.) that provides massive data. Those emerging big medical data can support the use of deep learning.

This Special Section in IEEE Access aims to provide a forum to present the latest advancements in deep learning research that directly concerns the computer-aided diagnosis community. It is especially important to develop deep networks to capture normal-appearing lesions, which may be neglected by human interpretation.

The topics of interest include, but are not limited to:

  • CAMD for neurodegenerative diseases, neoplastic disease, cerebrovascular disease, and inflammatory disease.
  • Deep learning and regularization techniques (Multi-task learning, autoencoder, sparse representation, dropout, batch normalization, convolutional neural network, transfer learning, etc.)
  • Novel training and inference methods for deep networks
  • Deep network architecture for CAMD and big medical data
  • Deep learning for cancer location, cancer image segmentation, cancer tissue classification, cancer image retrieval
  • Other medical signal and image processing related applications.

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Yu-Dong Zhang, University of Leicester, UK

Guest Editors:

  1. Zhengchao Dong, Columbia University, USA
  2. Carlo Cattani, University of Tuscia, Italy
  3. Shui-Hua Wang, Nanjing Drum Tower Hospital, China

 

Relevant IEEE Access Special Sections:

  1. New Trends in Brain Signal Processing and Analysis
  2. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
  3. Data Mining and Granular Computing in Big Data and Knowledge Processing


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: yudongzhang@ieee.org

New Trends in Brain Signal Processing and Analysis

Submission Deadline: 31 January 2019

IEEE Access invites manuscript submissions in the area of New Trends in Brain Signal Processing and Analysis.

Novel computational techniques in the field of neuroscience have been introduced in the literature and others are still under investigation. These studies cover a wide range of technological novelties such as controlled virtual environments that include higher cognitive and executive procedures and investigations of signals generated by the ailing brain, as found in posttraumatic disorders, cerebral palsy, or traumatic brain injury, for example. New trends in brain signal processing and analysis aim not only to study the pathology but also to explore ways to promote brain function recovery. Examples can include noninvasive brain stimulation (brain modulation), more accurate and faster algorithms than the traditional ones, neurorobotics and brain-machine interfaces. A better understanding of how our brain works with new realistic computational algorithms makes it possible to simulate and model specific brain functions for the development of new machine learning techniques.

The main objective of this Special Section in IEEE Access is to bring together recent advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to brain signal processing and analysis. We invite researchers to contribute original work related to the different fields of knowledge, such as neuroengineering, rehabilitation, psychology, pattern recognition, computational intelligence, machine learning and robotics, used in the context of understanding how the brain works, reacts, and adapts.

The topics of interest include, but are not limited to:

  • Brain Signal Processing Techniques
  • Brain Image Processing Techniques
  • Brain Source Estimation
  • Brain Network Analysis
  • The Brain in different realities: Virtual, Augmented, and Mixed
  • Brain-Machine Interface Systems
  • Brain-to-Brain Interaction
  • Automatic Detection and Diagnosis of Neurologic Diseases
  • Neurorobotics
  • Internet of Brain Things
  • EEG Biometrics

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil


Guest Editors:

  1. Alkinoos Athanasiou, Aristotle University of Thessaloniki, Greece.
  2. Robertas Damaševicius, Kaunas University of Technology, Lithuania.
  3. Pedro P. Rebouças Filho, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Brazil.
  4. Mohsen Guizani, University of Idaho, USA.

 

Relevant IEEE Access Special Sections:

  1. Information Security Solutions for Telemedicine Applications
  2. Recent Computational Methods in Knowledge Engineering and Intelligence Computation
  3. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  victor.albuquerque@unifor.br