Search for: medical-decision-making
Article 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008, Shanghai, 16 May 2008 through 18 May 2008 ; 2008 , Pages 281-284 ; 9781424417483 (ISBN) ; Niknam, S ; Sharif University of Technology
IEEE Computer Society 2008
This paper presents a mathematical model describing how the physicians actually make a diagnostic decision. Next to a description of diagnostic decision making as done by the physicians, this paper shows that how can we design and develop a new approach to medical diagnosis based on the human principles of decision making. The model was challenged to diagnose a series of actual patients. Real clinical data was entered into the model and the system produced a ranked list of possible diagnoses for each case. The results indicated good performance when compared with internist's diagnosis. The proposed method is effective and can be applied to describe medical reasoning as done by the...
Article Journal of Medical Engineering and Technology ; Volume 37, Issue 1 , 2013 , Pages 43-47 ; 03091902 (ISSN) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination...
An interactive cbir system based on anfis learning scheme for human brain magnetic resonance images retrieval, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 1 , 2012 , Pages 27-36 ; 10162372 (ISSN) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the...