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To Design a Mammogram Edge Detection Algorithm Using an Artificial Neural Network (ANN)

To Design a Mammogram Edge Detection Algorithm Using an Artificial Neural Network (ANN)
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Author(s): Alankrita Aggarwal (Panipat Institute of Engineering and Technology, Samalkha, India) and Deepak Chatha (Panipat Institute of Engineering and Technology, Samalkha, India)
Copyright: 2019
Volume: 11
Issue: 1
Pages: 10
Source title: International Journal of Distributed Artificial Intelligence (IJDAI)
Editor(s)-in-Chief: Firas A. Raheem (University of Technology - Iraq, Iraq) and Israa AbdulAmeer AbdulJabbar (University of Technology - Iraq, Iraq)
DOI: 10.4018/IJDAI.2019010104

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Abstract

An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.

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