Manuscript Topics
With the evolution of various modern technologies, huge amount of data is being constantly generated and collected around us. We are in the midst of what is popularly called information revolution and are living in a so-called world of knowledge. Big data is used in several research areas related to healthcare, location-based services, satellite information usage, online advertising, and retail marketing. It is a huge and challenging task to extract knowledge and build models with big data. Researchers and practitioners are trying to look into the future of big data to extract more benefits form biomedical datasets.

Currently, several attempts have been made to develop a cognitive/artificial intelligence systems to deal with biomedical big datasets, whcih is able to assist health professionals in their decision-making involving computational techniques of several levels, each handling a particular aspect of the problem. These systems are known to all as computer-aided diagnosis systems (CAD) and go beyond just signal and image processing in order to provide specific information about the lesion that can assist radiologists in the diagnosis, for instance. However, signal and image processing alone is not able to solve problems such as fatigue, distraction or limitations in training.

These systems can be divided into two approaches: detection system (CADe) and diagnostic system (CADx). The goal of a CADe system is to identify the relevant attributes in a database that can reveal specific abnormalities and alert physicians to these regions. A CADx system is to provide medical aid in the identification of the disease, its type, severity, stage, progression or regression. This latter system can either use only image informations or can combine other data relevant to the diagnosis. Some CAD systems can act as CADe and CADx systems by first identifying any potentially abnormal regions and then providing a quantitative or qualitative evaluation of the identified abnormalities.

There are still many challenging problems involved in improving the accuracy, efficiency, and usability of CADe and CADx systems and problems related to designing, developing, and deploying new applications. Thus, this special issue aims to introduce the recent progress of CADe and CADx systems and addresses the challenges in developing systems for various practical applications, while proposing new algorithms and/or impactful applications for future development ih the health field.

Potential topics include but are not limited to the following:
• Data preprocessing, feature extraction, recognition, and matching for CADe and CADx systems
• Image and Signal processing and machine learning techniques for CADe and CADx systems
• Multimodal (EEG, EMG, ECG, and other biosignals) Signal Processing
• Pattern recognition for CADe and CADx systems
• Performance and accuracy evaluation of CADe and CADx systems
• Information fusion for CADe and CADx systems
• CADe and CADx systems-based augmented reality and virtual reality applications
• Feature selection /extraction / construction/ recognition for medical image
• Data mining in evolutionary computation for biomedical decision-making system
• Machine learning for emotion computing and for large-scale image and multimedia processing
• Application of data processing technology in large-scale medicine and healthcare data
• Intelligent adaptive diagnosis and analysis in big medial data applications
• Structured spare representation for large scale medical image classificatio
• Serious games in health field
• Internet of Health things
• Health 4.0

IMPORTANT: Please choose “Cognitive and Intelligence methods for BDS” when specifying the special issue.

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