Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images
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 hu...
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| Format: | Article |
| Language: | en |
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Elsevier
2018
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| Online Access: | http://eprints.uthm.edu.my/5145/1/AJ%202018%20%28848%29%20Neural%20network%20and%20multi-fractal%20dimension%20features%20for%20breast%20cancer%20classification%20from%20ultrasound%20images.pdf http://eprints.uthm.edu.my/5145/ |
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| author | Mohammed, Mazin Abed Al-Khateeb, Belal Rashid, Ahmed Noori Ahmed Ibrahim, Dheyaa Abd Ghani, Mohd Khanapi A. Mostafa, Salama |
| author_facet | Mohammed, Mazin Abed Al-Khateeb, Belal Rashid, Ahmed Noori Ahmed Ibrahim, Dheyaa Abd Ghani, Mohd Khanapi A. Mostafa, Salama |
| author_sort | Mohammed, Mazin Abed |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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%). |
| format | Article |
| id | my.uthm.eprints-5145 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.uthm.eprints-51452022-01-06T02:36:08Z http://eprints.uthm.edu.my/5145/ Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images Mohammed, Mazin Abed Al-Khateeb, Belal Rashid, Ahmed Noori Ahmed Ibrahim, Dheyaa Abd Ghani, Mohd Khanapi A. Mostafa, Salama QA76 Computer software TA Engineering (General). Civil engineering (General) TA168 Systems engineering 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%). Elsevier 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5145/1/AJ%202018%20%28848%29%20Neural%20network%20and%20multi-fractal%20dimension%20features%20for%20breast%20cancer%20classification%20from%20ultrasound%20images.pdf 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 |
| spellingShingle | QA76 Computer software TA Engineering (General). Civil engineering (General) TA168 Systems engineering Mohammed, Mazin Abed Al-Khateeb, Belal Rashid, Ahmed Noori Ahmed Ibrahim, Dheyaa Abd Ghani, Mohd Khanapi A. Mostafa, Salama Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images |
| title | Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images |
| title_full | Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images |
| title_fullStr | Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images |
| title_full_unstemmed | Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images |
| title_short | Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images |
| title_sort | neural network and multi-fractal dimension features for breast cancer classification from ultrasound images |
| topic | QA76 Computer software TA Engineering (General). Civil engineering (General) TA168 Systems engineering |
| url | http://eprints.uthm.edu.my/5145/1/AJ%202018%20%28848%29%20Neural%20network%20and%20multi-fractal%20dimension%20features%20for%20breast%20cancer%20classification%20from%20ultrasound%20images.pdf http://eprints.uthm.edu.my/5145/ |
| url_provider | http://eprints.uthm.edu.my/ |