Department of Physic, University of Dschang,
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In healthcare field, data volume is increasingly growing each day, and traditional methods cannot manage it efficiently. In biomedical computation, the continuous challenges are: management, analysis, and storage of the biomedical data. Big data is often defined by five major characteristics called the “5V”: volume (amount of data generated), variety (data from different categories) and velocity (speed of data generation), variability (inconsistency of data) and veracity (quality of captured data). Big data in health is concerned with meaningful datasets that are too big, too fast, and too complex for healthcare providers to process and interpret with existing tools and methods. Nowadays, big data technology and artificial intelligence play a significant role in the management, organization, and analysis of data, using machine learning and deep learning techniques. Biomedical images are too important because they allow to visualize the pathologies and cancerogenic cells. Thus, we can use new methods based on machine learning and deep learning to monitor and interpret images. So, it becomes very important to develop methods and/or architectures based on big data technologies and artificial intelligence, for a complete processing of biomedical image data.