x-ray analysis; localization; convolutional neural networks; breast cancer
Abstract :
[en] The latest advances in machine learning and in particular with convolutional neurons (CNN) have proven more than once their great accuracy in the detection of anomalies.
Deep learning algorithms, in particular convolutional neural networks, have rapidly become a methodology of choice for analyzing medical images.
In this paper, we present a new approach for mass detection from mammogram X-ray images using Deep Learning algorithms. An efficient process based on Yolo v5 is proposed in this paper.
Experiments have been conducted using an anonymized database from a Belgium hospital thanks to a retrospective study. It is composed of two classes (benin, malin).
Disciplines :
Computer science
Author, co-author :
Lessage, Xavier
Benzaouia, Hajar
Murgo, Salvatore
Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Mahmoudi, Sidi ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Guessoum, Zahia
Language :
English
Title :
Deep Convolutional Neural Networks (CNN) for lesions localization of in mammograms
Publication date :
24 June 2022
Journal title :
International Journal of Computer Assisted Radiology and Surgery
ISSN :
1861-6410
eISSN :
1861-6429
Publisher :
Springer, Germany
Peer reviewed :
Peer Reviewed verified by ORBi
Research unit :
F114 - Informatique, Logiciel et Intelligence artificielle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
