Breast Cancer Detection Using Mammography: Image Processing to Deep Learning
Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniq...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10817580/ |
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| author | Shahzad Ahmad Qureshi Aziz-Ul-Rehman Lal Hussain Touseef Sadiq Syed Taimoor Hussain Shah Adil Aslam Mir Muhammad Amin Nadim Darnell K. Adrian Williams Tim Q. Duong Qurat-Ul-Ain Chaudhary Natasha Habib Asrar Ahmad Syed Adil Hussain Shah |
| author_facet | Shahzad Ahmad Qureshi Aziz-Ul-Rehman Lal Hussain Touseef Sadiq Syed Taimoor Hussain Shah Adil Aslam Mir Muhammad Amin Nadim Darnell K. Adrian Williams Tim Q. Duong Qurat-Ul-Ain Chaudhary Natasha Habib Asrar Ahmad Syed Adil Hussain Shah |
| author_sort | Shahzad Ahmad Qureshi |
| collection | DOAJ |
| description | Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations’ sustainability development goals. |
| format | Article |
| id | doaj-art-1323c6fa32bd46418ae14485147f582f |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1323c6fa32bd46418ae14485147f582f2025-08-20T02:16:33ZengIEEEIEEE Access2169-35362025-01-0113607766080110.1109/ACCESS.2024.352374510817580Breast Cancer Detection Using Mammography: Image Processing to Deep LearningShahzad Ahmad Qureshi0https://orcid.org/0000-0001-8213-1431 Aziz-Ul-Rehman1https://orcid.org/0000-0001-8605-5763Lal Hussain2https://orcid.org/0000-0003-1103-4938Touseef Sadiq3https://orcid.org/0000-0001-6603-3639Syed Taimoor Hussain Shah4Adil Aslam Mir5https://orcid.org/0000-0002-4183-1519Muhammad Amin Nadim6https://orcid.org/0000-0001-5106-0220Darnell K. Adrian Williams7https://orcid.org/0000-0003-1577-8168Tim Q. Duong8https://orcid.org/0000-0001-6403-2827Qurat-Ul-Ain Chaudhary9Natasha Habib10https://orcid.org/0009-0007-0123-9856Asrar Ahmad11Syed Adil Hussain Shah12Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, PakistanDepartment of Physics and Astronomy, Macquarie University, Sydney, NSW, AustraliaDepartment of Computer Science and Information Technology, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, PakistanDepartment of Information and Communication Technology, Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, NorwayDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMed Laboratory, Turin, ItalyDepartment of Computer Science and Information Technology, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, PakistanLearning Sciences and Digital Technologies, Università Telematica Pegaso, Naples, ItalyDepartment of Radiology, Albert Einstein College of Medicine, Bronx, NY, USADepartment of Radiology, Albert Einstein College of Medicine, Bronx, NY, USADepartment of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, PakistanDepartment of Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, PakistanDepartment of Medical Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, PakistanDepartment of Research and Development (R&D), GPI SpA, Trento, ItalyBreast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations’ sustainability development goals.https://ieeexplore.ieee.org/document/10817580/Breast cancermammographymicrocalcificationdeep learningconvolution neural networksmachine learning |
| spellingShingle | Shahzad Ahmad Qureshi Aziz-Ul-Rehman Lal Hussain Touseef Sadiq Syed Taimoor Hussain Shah Adil Aslam Mir Muhammad Amin Nadim Darnell K. Adrian Williams Tim Q. Duong Qurat-Ul-Ain Chaudhary Natasha Habib Asrar Ahmad Syed Adil Hussain Shah Breast Cancer Detection Using Mammography: Image Processing to Deep Learning IEEE Access Breast cancer mammography microcalcification deep learning convolution neural networks machine learning |
| title | Breast Cancer Detection Using Mammography: Image Processing to Deep Learning |
| title_full | Breast Cancer Detection Using Mammography: Image Processing to Deep Learning |
| title_fullStr | Breast Cancer Detection Using Mammography: Image Processing to Deep Learning |
| title_full_unstemmed | Breast Cancer Detection Using Mammography: Image Processing to Deep Learning |
| title_short | Breast Cancer Detection Using Mammography: Image Processing to Deep Learning |
| title_sort | breast cancer detection using mammography image processing to deep learning |
| topic | Breast cancer mammography microcalcification deep learning convolution neural networks machine learning |
| url | https://ieeexplore.ieee.org/document/10817580/ |
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