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.
A supervised breast lesion images classification from tomosynthesis technique / Bevilacqua, Vitoantonio; Altini, Daniele; Bruni, Martino; Riezzo, Marco; Brunetti, Antonio; Loconsole, Claudio; Guerriero, Andrea; Trotta, Gianpaolo Francesco; Fasano, Rocco; Di Pirchio, Marica; Tartaglia, Cristina; Ventrella, Elena; Telegrafo, Michele; Moschetta, Marco (LECTURE NOTES IN COMPUTER SCIENCE). - In: Intelligent Computing Theories and Application: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017, Proceedings, Part II / [a cura di] De-Shuang Huang, Kang-Hyun Jo, Juan Carlos Figueroa-García. - Cham, CH : Springer, 2017. - ISBN 978-3-319-63311-4. - pp. 483-489 [10.1007/978-3-319-63312-1_42]
A supervised breast lesion images classification from tomosynthesis technique
Bevilacqua, Vitoantonio;Bruni, Martino;Brunetti, Antonio;Loconsole, Claudio;Guerriero, Andrea;Trotta, Gianpaolo Francesco;
2017-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
