Breast cancer diagnosis: A systematic review

• Autorzy: Wen Xin , Guo Xing , Wang Shuihua , Lu Zhihai , Zhang Yudong

Identyfikatory

DOI

10.1016/j.bbe.2024.01.002

• Języki publikacji: EN

Abstrakty

EN

The second-leading cause of death for women is breast cancer. Consequently, a precise early diagnosis is essential. With the rapid development of artificial intelligence, computer-aided diagnosis can efficiently assist radiologists in diagnosing breast problems. Mammography images, breast thermal images, and breast ultrasound images are the three ways to diagnose breast cancer. The paper will discuss some recent developments in machine learning and deep learning in three different breast cancer diagnosis methods. The three components of conventional machine learning methods are image preprocessing, segmentation, feature extraction, and image classification. Deep learning includes convolutional neural networks, transfer learning, and other methods. Additionally, the benefits and drawbacks of different methods are thoroughly contrasted. Finally, we also provide a summary of the challenges and potential futures for breast cancer diagnosis.

Słowa kluczowe

EN

machine learning; deep learning; AI; breast cancer diagnosis; mammography image; ultrasound image; thermal image

PL

uczenie maszynowe; uczenie głębokie; rak piersi; obraz mammograficzny; obraz ultrasonograficzny; obraz termiczny

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 1
• Strony: 119--148
• Opis fizyczny: Bibliogr. 166 poz., rys., tab.

Twórcy

autor

Wen Xin

• School of Educational Science, Nanjing Normal University, Nanjing, Jiangsu 210023, China

autor

Guo Xing

• School of Educational Science, Nanjing Normal University, Nanjing, Jiangsu 210023, China

autor

Wang Shuihua

• School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
• Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China

autor

Lu Zhihai

• School of Educational Science, Nanjing Normal University, Nanjing, Jiangsu 210023, China

autor

Zhang Yudong

• School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
• Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
• School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).