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Poster communications

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

Kadda Djebbar Malika Mimi Khadidja Berradja Abdelmalik Taleb-Ahmed 1, 2 
2 COMNUM - IEMN - COMmunications NUMériques - IEMN
IEMN-DOAE - Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520
Abstract : 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).
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Submitted on : Monday, February 14, 2022 - 2:57:24 PM
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Kadda Djebbar, Malika Mimi, Khadidja Berradja, Abdelmalik Taleb-Ahmed. Deep convolutional neural networks for detection and classification of tumors in mammograms. 6th International Conference on Image and Signal Processing and their Applications (ISPA 2019), Nov 2019, Mostaganem, Algeria. IEEE, pp.1-7, ⟨10.1109/ISPA48434.2019.8966895⟩. ⟨hal-03573002⟩



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