Ontology based decision system for breast cancer diagnosis
| Autor: | Laurent Wendling, Dorra Sellami, Soumaya Trabelsi Ben Ameur, Florence Cloppet |
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| Rok vydání: | 2018 |
| Předmět: |
Modality (human–computer interaction)
Breast imaging business.industry Computer science Semantic analysis (machine learning) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Decision tree Ontology (information science) medicine.disease Machine learning computer.software_genre Support vector machine ComputingMethodologies_PATTERNRECOGNITION Breast cancer medicine Artificial intelligence business computer Semantic gap |
| Zdroj: | ICMV |
| Popis: | In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis. |
| Databáze: | OpenAIRE |
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