Image Pattern Recognition for an Intelligent Healthcare System: An Application Area of Machine Learning and Big Data
Today healthcare sector is completely distinguished from other industries. It is a highly important area and people wants highest level of care and facilities irrespective of cost. It could not accomplish social prospect even though it consumes vast fraction of budget. Frequently the
analyses of medical data were done by the medical expert. In terms of image analysis by different human expert, it is often restricted due to its subjectivity, image complexity, widespread differences occur across different translators, and fatigue. As after the feat of Big Data and machine
learning in real world medical application, it is similarly giving exhilarating results with fine precision for medical imaging and is viewed as an important factor for upcoming applications in area of health sector. This paper presents survey of different applications on the Machine Learning
and Big Data which relies on image pattern recognition.
Keywords: Big Data; Healthcare System; Image Recognition; Machine Learning
Document Type: Research Article
Affiliations: Department of CSE, Maharishi Markandeshwar Deemed to University, Mullana, Ambala (Haryana) 133207, India
Publication date: 01 September 2019
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
- Editorial Board
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content