Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences

Bottigli, U., Chiarucci, R., Golosio, B., Masala, Giovanni Luca, Oliva, P., Stumbo, S., Cascio, D., Fauci, F., Glorioso, M., Iacomi, M., and others. (2006) Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences. International Journal of Biomedical Sciences, 1 (1). pp. 56-62. ISSN 1306-1216. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:92289)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.

Abstract

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an 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 generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic)curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.

Item Type:	Article
Uncontrolled keywords:	Neural Networks; K-Nearest Neighbours; Support Vector Machine; Computer Aided Detection
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Amy Boaler
Date Deposited:	13 Dec 2021 14:29 UTC
Last Modified:	13 Dec 2021 14:29 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/92289 (The current URI for this page, for reference purposes)

University of Kent Author Information

Masala, Giovanni Luca.

Creator's ORCID:	https://orcid.org/0000-0001-6734-9424
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