thermogram Breast Cancer Detection : a comparative study of two machine learning techniques

AlFayez, F, El-Soud, MWA and Gaber, T ORCID: https://orcid.org/0000-0003-4065-4191 2020, 'thermogram Breast Cancer Detection : a comparative study of two machine learning techniques' , Applied Sciences, 10 (2) , e551.

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Abstract

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

Item Type: Article
Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2076-3417 **History: published 11-01-2020; accepted 06-01-2020
Uncontrolled Keywords: Thermogramm, homomorphic filtering, top-hat transform, adaptive histogram, equalization, K-mean, signature boundary, MLP, ELM
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Applied Sciences
Publisher: MDPI
ISSN: 2076-3417
Related URLs:
Funders: Research Center for engineering and applied sciences and College of Science at Zulfi City, Majmaah University, Saudi Arabia
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 14 Jan 2020 11:30
Last Modified: 14 Jan 2020 11:31
URI: http://usir.salford.ac.uk/id/eprint/56206

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