In this paper, we propose a deep learning approach for breast lesions classification, by processing breast images obtained using an innovative acquisition system, the Tomosynthesis, a medical instrument able to acquire high-resolution images using a lower radiographic dose than normal Computed Tomography (CT). The acquired images were processed to obtain Regions Of Interest (ROIs) containing lesions of different categories. Subsequently, several pre-trained Convolutional Neural Network (CNN) models were evaluated as feature extractors and coupled with non-neural classifiers for discriminate among the different categories of lesions. Results showed that the use of CNNs as feature extractor and the subsequent classification using a non-neural classifier reaches high values of Accuracy, Sensitivity and Specificity.
|Titolo:||A supervised breast lesion images classification from tomosynthesis technique|
|Titolo del libro:||Intelligent Computing Theories and Application: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017, Proceedings, Part II|
|Data di pubblicazione:||2017|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-319-63312-1_42|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|