caa a22 4500 605447608 CHVBK 20210128100132.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-013-1792-2 doi (NATIONALLICENCE)springer-10.1007/s11042-013-1792-2 IFM3IRS: Information fusion retrieval system with knowledge-assisted text and visual features based on medical conceptual model [Elektronische Daten] [Hizmawati Madzin, Roziati Zainuddin, Nurfadhlina Sharef] The technology of medical data production has been rapidly changed over the past few years. Modern computer technology has created the possibility of creating multi-modal medical images. Medical data often contain multi-modal information such as visual information (image) as well as textual information. Both types of information are important for medical retrieval system (MRS). Due to the information limitation at different levels of sources, the application of information fusion becomes a real need in medical application. In this research, an information fusion framework was built to develop the multi-modality medical image retrieval system (IFM3IRS). The framework utilizes two sources of information involving text and visual-based retrieval process. The application is based on sequential order where the result from text-based process will automatically be the input in visual-based process. The main contributions of this paper are the development of a new ranking model called MedHieCon ranking model which applies semantic concepts of modality, anatomy and pathology in text-based process and also the learning approach of medical images using medical concept model in visual-based process. ImageCLEFmed 2010 data collection was used to evaluate IFM3IRS and it shows that our information fusion framework is in top list among other researchers. Although text-based retrieval system has proven to be a better performance in MRS; it is significant to determine the overall performance improvements which include the fusion of text and image. Springer Science+Business Media New York, 2013 Information fusion nationallicence Late fusion technique nationallicence Text-based retrieval nationallicence Visual-based image retrieval nationallicence Query expansion nationallicence Boolean model nationallicence Supervised classification nationallicence Madzin Hizmawati Multimedia Department, Faculty of Computer Science & Information Technology, University Putra Malaysia, Serdang, Malaysia aut Zainuddin Roziati Artificial Intelligence Department, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia aut Sharef Nurfadhlina Computer Science Department, Faculty of Computer Science & Information Technology, University Putra Malaysia, Serdang, Malaysia aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3651-3674 1380-7501 74:11<3651 2015 74 11042 https://doi.org/10.1007/s11042-013-1792-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1792-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Madzin Hizmawati Multimedia Department, Faculty of Computer Science & Information Technology, University Putra Malaysia, Serdang, Malaysia aut NATIONALLICENCE 700 1- Zainuddin Roziati Artificial Intelligence Department, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia aut NATIONALLICENCE 700 1- Sharef Nurfadhlina Computer Science Department, Faculty of Computer Science & Information Technology, University Putra Malaysia, Serdang, Malaysia aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3651-3674 1380-7501 74:11<3651 2015 74 11042