Impact Score 1.81
M. Shamim Hossain, King Saud University, Saudi Arabia ([email protected])
Josu Bilbao, IKERLAN, Spain ([email protected])
Diana P. Tobón, Universidad de Medellín, Colombia ([email protected])
Ghulam Muhammad, King Saud University, Saudi arabia ([email protected])
Abdulmotaleb El Saddik, University of Ottawa, Canada ([email protected])
Digital health generates a huge amount of multimedia healthcare data in the form of text, radiological images, audio, video and so forth. Since the start of the COVID-19 pandemic, we have witnessed an incremental increase in the present healthcare data. Such large-scale multimedia healthcare data creates challenges and opportunities for multimedia healthcare data analysis. AI, and more specifically deep learning (DL) algorithms, have been widely used by researchers for handling the massive volume of epidemic data, predicting the live epidemic crisis and initiating new research directions to analyze healthcare multimedia data. Therefore, deep learning for multimedia healthcare data analysis is becoming an emerging research area in the field of multimedia and computer vision.
This special issue is intended to report high-quality research on recent advances in Deep Learning for multimedia healthcare, specifically state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of those convergent technologies for providing insights into multimedia healthcare service demands. Authors are solicited to submit unpublished papers in the following topics. Topic include but are not restricted to:
Papers submitted to this special issue must be original and must not be under consideration for publication in any other journal or conference. The papers must be written in English and must not exceed 30 pages (single column, double space, 12 pt font, including figures, tables, and references). Authors must follow the journal's formatting and submission instructions.