Deep learning model for fully automated breast cancer detection system from thermograms

Mohamed, EA ORCID: https://orcid.org/0000-0001-6877-9610, Rashed, EA ORCID: https://orcid.org/0000-0001-6571-9807, Gaber, T ORCID: https://orcid.org/0000-0003-4065-4191 and Karam, O 2022, 'Deep learning model for fully automated breast cancer detection system from thermograms' , PLOS ONE, 17 (1) , e0262349.

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Abstract

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

Item Type: Article
Contributors: Damaševičius, R (Editor)
Additional Information: ** From PLOS via Jisc Publications Router ** Licence for this article: http://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1932-6203 **Article IDs: publisher-id: pone-d-21-28729 **History: published_online 14-01-2022; collection 2022; accepted 22-12-2021; submitted 09-09-2021
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: PLOS ONE
Publisher: Public Library of Science
ISSN: 1932-6203
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 17 Jan 2022 11:16
Last Modified: 15 Feb 2022 16:51
URI: https://usir.salford.ac.uk/id/eprint/62793

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