Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images

Mohammed, Mazin Abed and Al-Khateeb, Belal and Rashid, Ahmed Noori and Ahmed Ibrahim, Dheyaa and Abd Ghani, Mohd Khanapi and A. Mostafa, Salama (2018) Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Computers and Electrical Engineering, 70. pp. 871-882. ISSN 0045-7906

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Abstract

Breast cancer is considered to be one of the most threatening issues in clinical practice. However, existing breast cancer diagnosis methods face questions of complexity, cost, human-dependency, and inaccuracy. Recently, many computerized and interdisciplinary systems have been developed to avoid human errors in both quantification and diagnosis. A computerized system can be further improved to optimize the efficiency of breast tumour identification. The current paper presents an effort to automate characterization of breast cancer from ultrasound images using multi-fractal dimensions and backpropagation neural networks. In this study, a total of 184 breast ultrasound images (72 abnormal (tumour cases) and 112 normal cases) were examined. Various setups were employed to achieve a decent balance between positive and negative rates of the diagnosed cases. The obtained results manifested in high rates of precision (82.04%), sensitivity (79.39%), and specificity (84.75%).

Item Type:	Article
Uncontrolled Keywords:	Breast cancer; Multi-fractal dimension; Neural network approach; Breast cancer classification; Ultrasound images
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
Divisions:	Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User:	UiTM Student Praktikal
Date Deposited:	06 Jan 2022 02:36
Last Modified:	06 Jan 2022 02:36
URI:	http://eprints.uthm.edu.my/id/eprint/5145

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