Visual classification of medical data using MLP mapping

• Published in: Computers in biology and medicine 1998-05, Vol.28 (3), p.275-287
• Main Authors: Güler, Emin Çağatay, Sankur, Bülent, Kahya, Yasemin P., Raudys, Sarunas
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Automatic classification; Biological and medical sciences; Classification; Computer Graphics; Computerized, statistical medical data processing and models in biomedicine; Data Display; Diagnosis, Computer-Assisted - methods; Humans; Linear Models; Lung Diseases - diagnosis; Lung Diseases - physiopathology; Lung Diseases, Obstructive - diagnosis; Lung Diseases, Obstructive - physiopathology; Mapping; Medical computing and teaching; Medical sciences; Medicine; Multilayer perceptrons; Neural Networks (Computer); Nonlinear Dynamics; Respiration; Respiratory sound patterns; Respiratory Sounds - diagnosis; Respiratory Sounds - physiopathology; Visual displays; Visualization
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/S0010-4825(98)00010-9

In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value.