Biomaterial Database

Formal design methods for reliable computer-aided diagnosis: a review. 2012

Oliver Faust, and U Rajendra Acharya, and Toshiyo Tamura

Department of Electrical and Computer Engineering, Ngee Ann Polytechnic University, Singapore. fol2@np.edu.sg

Physiological signals, medical images, and biosystems can be used to access the health of a subject and they can support clinicians by improving the diagnosis for treatment purposes. Computer-aided diagnosis (CAD) in healthcare applications can help in automated decision making, visualization and extraction of hidden complex features to aid in the clinical diagnosis. These CAD systems focus on improving the quality of patient care with a minimum of fault due to device failures. In this paper, we argue that a formal and model driven design methodology can lead to systems which meet this requirement. Modeling is not new to CAD, but modeling for systems design is less explored. Therefore, we discuss selected systems design techniques and provide a more concrete design example on computer-aided diagnosis and automated decision making.

UI	MeSH Term	Description	Entries
D003936	Diagnosis, Computer-Assisted	Application of computer programs designed to assist the physician in solving a diagnostic problem.	Computer-Assisted Diagnosis,Computer Assisted Diagnosis,Computer-Assisted Diagnoses,Diagnoses, Computer-Assisted,Diagnosis, Computer Assisted
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001698	Biomedical Engineering	Application of principles and practices of engineering science to biomedical research and health care.	Clinical Engineering,Engineering, Clinical,Engineering, Biomedical
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron