Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% +/- 1 of area under ROC curve obtained with the Feed Forward Neural Network.

Bottigli, U., Cascio, D., Fauci, F., Golosio, B., Magro, R., Masala, G.L., et al. (2006). Massive lesions classification using features based on morphological lesion differences. In PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY (pp.20-24). New York : World Academy of Science Engineering and Technology.

Massive lesions classification using features based on morphological lesion differences

Bottigli, U.;
2006

Abstract

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% +/- 1 of area under ROC curve obtained with the Feed Forward Neural Network.
Bottigli, U., Cascio, D., Fauci, F., Golosio, B., Magro, R., Masala, G.L., et al. (2006). Massive lesions classification using features based on morphological lesion differences. In PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY (pp.20-24). New York : World Academy of Science Engineering and Technology.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/432915