Title: Multi-agents system for breast tumour detection in mammography by deep learning pre-processing and watershed segmentation

Authors: Hayet Saadi; Hayet Farida Merouani; Ahlem Melouah; Zahia Guessoum; Saida Lemnadjlia; Nacereddine Boukabach

Addresses: Computer Sciences Department, Laboratoire de Recherche en Informatique (LRI), Badji Mokhtar University, BP 12, Annaba, Algeria ' Computer Sciences Department, Laboratoire de Recherche en Informatique (LRI), Badji Mokhtar University, BP 12, Annaba, Algeria ' Computer Sciences Department, Laboratoire de Recherche en Informatique (LRI), Badji Mokhtar University, BP 12, Annaba, Algeria ' Computer Sciences Department, Laboratoire d'Informatique de Paris 6 (LIP6), Sorbonne University, 75006, Paris, France ' Computer Sciences Department, Laboratoire de Recherche en Informatique (LRI), Badji Mokhtar University, BP 12, Annaba, Algeria ' Radilogist at Medical Imaging Center, 17 Rue de l'Independance, Azzaba, Algeria

Abstract: Mammography is the most used process for females to diagnose and screen breast cancer. In this paper, we presented an enhanced automatic watershed segmentation for breast tumour detection and segmentation reinforced with a group of interactive agents. First, we started by a pre-processing based on deep learning (DL), where a convolution neural network (CNN) is applied, to classify the breast density by AlexNet architecture. Second, classic watershed segmentation was applied on these images. Afterward, a multi-agents system (MASs) was introduced. The information within pixels, regions and breast density were explored, to create a region of interest (ROI), and to emerge the MAS segmentation. Experimental results were promising in term of accuracy (ACC), with an overall of 97.18% over three datasets, Mammographic Image Analysis Society (MIAS), INBreast, and a local dataset called Database of Digital Mammograms of Annaba (DDMA). In some cases, our approach was able to detect breast calcification accurately.

Keywords: mammography; tumour detection; watershed segmentation; multi-agent systems; multi-agents system; deep learning; convolution neural network; AlexNet architecture; pre-processing; breast density; computational vision; computer-aided diagnosis systems.

DOI: 10.1504/IJCVR.2022.126506

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.6, pp.632 - 661

Received: 01 Jul 2021
Accepted: 10 Oct 2021
Published online: 27 Oct 2022 *

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