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An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms

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Abstract

Breast cancer is the most common cancer in women worldwide and the second main cause of cancer mortality after lung cancer. Up to now, there still no prevention nor early symptoms of breast cancer. Early detection can decrease significantly the mortality rate as the disease can be treated at an early stage. X-Ray is the current screening method that helps in detecting the most two common abnormalities of the breast, masses and micro-calcifications. However, interpreting mammograms is challenging in dense breasts as the abnormal masses and the normal glandular tissue of the breast have similar characteristics. Recently, the evolutionary algorithms have been widely used in image segmentation. In this paper, we evaluate and compare the performance of six most used evolutionary algorithms, invasive weed optimization (IWO), genetic algorithm (GA), particle swarm optimization (PSO), electromagnetism-like optimization (EMO), ant colony optimization (ACO), and artificial bee colony (ABC) in terms of clustering abnormal masses in the breast, particularly dense and extremely dense breasts. This evaluation is conducted based on quantitative metrics including Cohen’s Kappa, correlation, and false positive and false negative rates. The evolutionary algorithms are then ranked based on two multi-criteria decision analysis methods, the Preference Ranking Organization Method for the Enrichment of Evaluations (PROMETHEE) and the Graphical Analysis for Interactive Aid (GAIA).

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Authors and Affiliations

  1. National Institute of Posts and Telecommunications (INPT), STRS Lab, Rabat, Morocco

    Khaoula Belhaj Soulami & Ahmed Tamtaoui

  2. Electrical Engineering Department, University of North Dakota, Grand Forks, USA

    Khaoula Belhaj Soulami & Naima Kaabouch

  3. National Institute of Statistic and Applied Economy INSEA Laboratory of Information Systems SI2M, Rabat, Morocco

    Mohamed Nabil Saidi

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  1. Khaoula Belhaj Soulami
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  2. Naima Kaabouch
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Belhaj Soulami, K., Kaabouch, N., Saidi, M.N. et al. An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms. Multimed Tools Appl 79, 18941–18979 (2020). https://doi.org/10.1007/s11042-019-08449-5

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