Title :

Deep convolutional neural networks for detection and classification of tumors in mammograms

Author(s) :

Djebbar, Kadda [Auteur]
Mimi, Malika [Auteur]
Berradja, Khadidja [Auteur]
Tahleb Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

6th International Conference on Image and Signal Processing and their Applications (ISPA 2019)

City :

Mostaganem

Country :

Algérie

Start date of the conference :

2019-11-24

Publisher :

IEEE

English keyword(s) :

Breast Cancer
Mass Detection and Classification
Computer Aided Diagnosis
Deep Learning
YOLO

HAL domain(s) :

Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique

English abstract : [en]

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis ...
Show more >Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD).System based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO), in this work we used YOLO version three (YOLOv3). YOLO based CAD system can handle detection and classification simultaneously in one framework. It's a little bigger than last time but more accurate. The proposed CAD system contains four steps : preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using fully connected neural networks (FC-NNs).Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL