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

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.