Microcalcification Detection in Mammograms Using Particle Swarm Optimization and Probabilistic Neural Network

Authors

  • Rachida Touami Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf
  • Nacéra Benamrane Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf

DOI:

https://doi.org/10.13053/cys-25-2-3429

Keywords:

Breast cancer, mammography, microcalcification, region growing segmentation, particle swarm optimization, probabilistic neural network

Abstract

Breast cancer is the most typical form of cancer among the female population and the most common form of cancer-related death. However, if the cancer is detected at an early stage, treatment may be more effective. Mammography is one of the most used imaging modalities for the early breast cancer diagnosis. The present paper proposes an intelligent system for the detection and analysis of microcalcifications in mammography using the region growing algorithm, the particle swarm optimization algorithm (PSO), and the Probabilistic neural network (PNN) to detect the presence of breast cancer as early as possible and to avoid resorting to ablation of the breast.

Author Biographies

Rachida Touami, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf

Laboratoire SIMPA, Département d’Informatique, Faculté des Mathématiques et d’Informatique

Nacéra Benamrane, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf

Laboratoire SIMPA, Département d’Informatique,Faculté des Mathématiques et d’Informatique

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Published

2021-05-01

Issue

Vol. 25 No. 2 (2021): 25(2) 2021

Section

Articles

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