Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review
Breast cancer (BC) is the leading cause of death among women worldwide. Early detection and diagnosis of BC can help significantly reduce the mortality rate. Ultrasound (US) can be an ideal screening tool for BC detection. However, the hand-held US (HHUS) is an impractical tool because it is operato...
| Main Authors: | , , , , |
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| Format: | Article |
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Springer Nature
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/108339/ |
| _version_ | 1848865135905472512 |
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| author | Pengiran Mohamad, Dayangku Nur Faizah Mashohor, Syamsiah Mahmud, Rozi Hanafi, Marsyita Bahari, Norafida |
| author_facet | Pengiran Mohamad, Dayangku Nur Faizah Mashohor, Syamsiah Mahmud, Rozi Hanafi, Marsyita Bahari, Norafida |
| author_sort | Pengiran Mohamad, Dayangku Nur Faizah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Breast cancer (BC) is the leading cause of death among women worldwide. Early detection and diagnosis of BC can help significantly reduce the mortality rate. Ultrasound (US) can be an ideal screening tool for BC detection. However, the hand-held US (HHUS) is an impractical tool because it is operator-dependent, time-consuming, and increases the likelihood of false-positive results. Thus, to address these issues, the 3D Automated Breast Ultrasound System (ABUS) was designed for BC detection and diagnosis. This paper presents the transition from traditional approaches to deep learning (DL) based CAD systems in the ABUS image data set. The capabilities and limitations of both techniques are also reviewed rigorously. This review will help in understanding the current limitations to leverage their potential in diagnostic radiology to improve performance and BC patient care. |
| first_indexed | 2025-11-15T13:59:54Z |
| format | Article |
| id | upm-108339 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:59:54Z |
| publishDate | 2023 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1083392025-05-30T07:35:36Z http://psasir.upm.edu.my/id/eprint/108339/ Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review Pengiran Mohamad, Dayangku Nur Faizah Mashohor, Syamsiah Mahmud, Rozi Hanafi, Marsyita Bahari, Norafida Breast cancer (BC) is the leading cause of death among women worldwide. Early detection and diagnosis of BC can help significantly reduce the mortality rate. Ultrasound (US) can be an ideal screening tool for BC detection. However, the hand-held US (HHUS) is an impractical tool because it is operator-dependent, time-consuming, and increases the likelihood of false-positive results. Thus, to address these issues, the 3D Automated Breast Ultrasound System (ABUS) was designed for BC detection and diagnosis. This paper presents the transition from traditional approaches to deep learning (DL) based CAD systems in the ABUS image data set. The capabilities and limitations of both techniques are also reviewed rigorously. This review will help in understanding the current limitations to leverage their potential in diagnostic radiology to improve performance and BC patient care. Springer Nature 2023-06 Article PeerReviewed Pengiran Mohamad, Dayangku Nur Faizah and Mashohor, Syamsiah and Mahmud, Rozi and Hanafi, Marsyita and Bahari, Norafida (2023) Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review. Artificial Intelligence Review, 56 (12). pp. 15271-15300. ISSN 0269-2821; eISSN: 1573-7462 https://link.springer.com/article/10.1007/s10462-023-10511-6?error=cookies_not_supported&code=6c5d3ca9-1907-4ef5-9a2f-5ad670906e4e 10.1007/s10462-023-10511-6 |
| spellingShingle | Pengiran Mohamad, Dayangku Nur Faizah Mashohor, Syamsiah Mahmud, Rozi Hanafi, Marsyita Bahari, Norafida Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review |
| title | Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review |
| title_full | Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review |
| title_fullStr | Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review |
| title_full_unstemmed | Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review |
| title_short | Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review |
| title_sort | transition of traditional method to deep learning based computer-aided system for breast cancer using automated breast ultrasound system (abus) images: a review |
| url | http://psasir.upm.edu.my/id/eprint/108339/ http://psasir.upm.edu.my/id/eprint/108339/ http://psasir.upm.edu.my/id/eprint/108339/ |