Guest Editors
Dr. Hum Yan Chai, Universiti Tunku Abdul Rahman, Malaysia.
Dr. Lai Khin Wee, Universiti Malaya, Malaysia.
Dr. Ong Hwai Chyuan, University of Technology Sydney, Australia.
Dr. Pengjian Qian, Jiangnan University, China.
Summary
With the advancement of digitalization, enormous amount of medical data in various forms are generated in healthcare centers daily. To leverage this proliferation of information in modern medical facilities for deriving insights, pertinent information from these data can be interpreted automatically to enhance the outcome of prognosis and diagnosis on different clinical complications such as pneumonia, heart failure and bone fracture.
Current workflow to avail of these information is tedious and invariably hinges on human experts such as radiologists to analyze and make decision. Therefore, this process is subject to human errors. Recent development of machine learning techniques, particularly, deep learning, and also the computational technologies, a substantial number of algorithms have been designed to aid clinical practitioner to perform the tasks in analyzing the medical data.
This special issue emphasizes on the establishment of expert systems applied on medical data using recent advances in computational intelligence technologies such as transfer learning, classification, detection, enhancement, segmentation, convolutional neural network(CNN), Long Short-Term Memory (LSTM), clustering, semi-supervised and unsupervised learning. The purpose of this special issue is to gain an overview of the progress and breakthrough particularly in the application of machine learning in medical imaging and healthcare analytics.
Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.
Dissemination, Composition and Review Procedures
• A Call for Papers (CFP) will be circulated to invite submissions.
• World leading researchers will be invited as authors.
• To further attracting contributors from around the world, the CFP will be advertised across numerous society newsletters, different websites, mailing lists, conferences, associations, and social media groups, etc
This special issue will run as per the timeline given from submission to publication, while maintaining the rigorous peer review and high standards of the journal. All manuscripts submitted must be original, not under consideration elsewhere, and not previously published.
A guide for authors and other relevant information for submission of manuscripts are available on the submission guidelines’ page. Authors can expect their manuscripts to be reviewed fairly, and in a skilled, conscientious manner. To enhance objectivity, and to guarantee high scientific quality and relevance to the subject, at least two peer reviewers will be selected to evaluate manuscript. The peer review process shall be designed to avoid bias and conflict of interest on the part of reviewers and shall be composed of experts in the relevant field of research. A key criterion in publication decisions will be the manuscript’s fit for the special issue and the readership of the journal. Papers will be published online as soon as accepted in continuous flow.
Keywords
• Medical image registration using machine learning
• Application of object localization using machine learning
• Segmentation techniques applied in medical imaging using machine learning
• Contrast enhancement technique applied on different imaging modalities
• Lesion classification and detection using machine learning
• Medical image analysis using computational intelligence
• Sensors technologies in healthcare applications
• Data analytics using medical data
• Computer aided system in healthcare application
• Deep learning technologies on medical data
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