The Concept of an Artificial Neural Network for the Classification of Atheromous Plaques from Digitized Segmented Histological Images
| Autor: | Jiri Blahuta, Jakub Skacel, Petr Cermak |
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| Rok vydání: | 2018 |
| Předmět: |
030219 obstetrics & reproductive medicine
Artificial neural network Computer science business.industry Plaque composition Supervised learning Feature extraction Pattern recognition Image segmentation Region growing algorithm 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Homogeneous Artificial intelligence business |
| Zdroj: | 2018 14th International Computer Engineering Conference (ICENCO). |
| DOI: | 10.1109/icenco.2018.8636116 |
| Popis: | This paper is dedicted to the concept of an artificial neural network (ANN) for the classification of atheromatous plaque based on digitized histological patterns of areas segmented by means of the Region Growing algorithm. For this purpose, a multi-layered feedforward ANN with supervised learning has been used to successfully classify the segmented areas accordingly. The first phase is focused to find an optimal method for image segmentation. The Region Growing algorithm allows us to separate continuously segmented regions. For each region, appropriate features are selected, which are put into the neural network. The goal of the ANN is to classify plaque patterns into four classes according to their features. The classes represent the following types of plaque: homogeneous, heterogeneous, calcified and that with a high ratio of fat.. Successful plaque classification will be a helpful tool for long-term clinical projects, e.g. investigation of plaque composition. |
| Databáze: | OpenAIRE |
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