Title: Mammogram mass segmentation using evolutionary algorithm-based single layer neural network

Authors: Sunita Sarangi; Harish Kumar Sahoo

Addresses: Department of Electronics and Communication Engineering, ITER, SOA University, Bhubaneswar, India ' Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, India

Abstract: Mammography is the most reliable method for detecting breast cancer in its early stages. Breast region segmentation is a fundamental procedure for analysing mammograms. This paper presents an improved segmentation approach using a hybrid model using a functional link artificial neural network (FLANN) based on particle swarm optimisation (PSO). The suggested segmentation technique makes use of a threshold for segmentation that is adaptively adjusted by the image attributes. A comparison has been made between three expansion techniques used for input to the FLANN, they are exponential FLANN (EFLANN), Chebyshev FLANN (CFLANN), and Legendre FLANN (LFLANN). 110 images from mini-MIAS and DDSM databases are used for comparison. The performance measures for CFLANN and LFLANN are found to be better than Exponential FLANN (EFLANN).

Keywords: mammogram; adaptive threshold; EFLANN; CFLANN; LFLANN; particle swarm optimisation; PSO.

DOI: 10.1504/IJCVR.2026.155185

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.1, pp.19 - 31

Received: 04 Aug 2023
Accepted: 07 Mar 2024
Published online: 29 Jul 2026 *

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