Feed Forward Neural Network for Autofluorescence Imaging Classification

• Authors: Z. Kulas , E. Bereś-Pawlik , J. Wierzbicki
• Languages of publication: EN

Abstracts

EN

The key elements in cancer diagnostics are the early identification and estimation of the tumor growth and its spread in order to determine the area to be operated on. The aim of our study was to develop new methods of analyzing autofluorescence images which will allow us an objective and accurate assessment of the location of a tumor and will also be helpful in determining the advancement of the disease. The proposed classification methods are based on neural network algorithms. An Olympus company endoscopic system was used for an autofluorescence intestine imaging study. The autofluorescence imaging analysis process can be divided into several main stages. The first step is preparation of a training data set. The second one involves selection of feature space, namely the selection of those features which enable distinguishing the pathologically altered areas from the healthy ones. Final stages of the analysis include pathologically changed tissue classification and diagnosis.

Keywords

EN

87.57.-s; 87.57.R-; 87.57.nm; 87.19.xu; 87.19.xj

Discipline

• 87.57.-s: Medical imaging
• 87.57.R-: Computer-aided diagnosis
• 87.19.xj: Cancer
• 87.19.xu: Gastrointestinal diseases
• 87.57.nm: Segmentation

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2010
• Volume: 118
• Issue: 6
• Pages: 1189-1193

Dates

published

2010-12

Contributors

author

Z. Kulas

• Institute of Telecommunications, Teleinformatics and Acoustics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

author

E. Bereś-Pawlik

• Institute of Telecommunications, Teleinformatics and Acoustics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

author

J. Wierzbicki

• Department of Miniinvasive and Proctological Surgery, Wrocław Medical University, Borowska 213, 50-556 Wrocław, Poland

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Identifiers

DOI

10.12693/APhysPolA.118.1189