title: Application of the fuzzy ART/MAP and MINMAX/MAP neural network models to radiographic image classification.
abstract: This paper concerns the classification analysis of exercise-induced lower leg pain by applying competitive neural network clustering and mapping techniques to type 1 and type 2 fuzzy descriptions of bone scan images of the tibia. The clusters are described and compared with each other and with the experts known classes that would be expected from medical findings. The discovered clusters provide training sets for supervised learning by an ARTMAP and similar neural network. These were used to classify the previously unclassified images and hence improve the classification process. The overall conclusion is that the use of the neural clustering methods has improved the classification process of the shin images despite the paucity of data and its inherent uncertainty.
authors.affiliation: Department of Computer Science, De Montfort University, Leicester, UK. pri@dmu.ac.uk
authors.forename: P R
authors.initials: PR
authors.lastname: Innocent
authors.forename: M
authors.initials: M
authors.lastname: Barnes
authors.forename: R
authors.initials: R
authors.lastname: John
mesh_terms: D001265:Athletic Injuries / Q000145:classification / Q000000981:diagnostic imaging; D001842:Bone and Bones / Q000000981:diagnostic imaging; D016000:Cluster Analysis; D003198:Computer Simulation; D015775:Fractures, Stress / Q000145:classification / Q000000981:diagnostic imaging; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D011859:Radiography