A comparison of recent progress in breast cancer diagnosis models using machine learning algorithms

Research Article
Open access

A comparison of recent progress in breast cancer diagnosis models using machine learning algorithms

Zhiyu Zhang 1*
  • 1 University of Electronic Science and Technology of China    
  • *corresponding author 2022080905029@std.uestc.edu.cn
Published on 21 February 2024 | https://doi.org/10.54254/2755-2721/39/20230586
ACE Vol.39
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-303-6
ISBN (Online): 978-1-83558-304-3

Abstract

Breast cancer is the leading cause of mortality among women suffering from cancer, so the accurate diagnosis is important. This review aims to provide a thorough examination of advancements and trends in breast cancer diagnosis by analysing recognized papers published between 2020 and 2023.The paper firstly gives a brief overview of breast cancer, machine learning algorithms, followed by an introduction of basic process for ML in breast cancer diagnosis. After that, by focusing on two emerging trends, hybridization and newly invented modalities, the review introduces existing achievements in the field. Subsequently, it highlights nine notable or novel designs in breast cancer diagnosis, while presenting their comparative properties in a tabular format. Hopefully, this review can equip researchers with valuable insights for future studies and references, helping them gain a better understanding of the field and facilitating further improvements in breast cancer detection and classification.

Keywords:

Breast Cancer, Computer-Aided Diagnosis, Machine Learning Algorithms

Zhang,Z. (2024). A comparison of recent progress in breast cancer diagnosis models using machine learning algorithms. Applied and Computational Engineering,39,103-113.
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References

[1]. Anon, n.d. Who launches new roadmap on breast cancer. [online] World Health Organization. Available from: https://www.who.int/news/item/03-02-2023-who-launches-new-roadmap-on-breast-cancer [Accessed 31 Aug. 2023].

[2]. Anderson, B.O.,, Braun, S.,, Lim, S.,, Smith, R.A.,, Taplin, S., and Thomas, D.B., 2003. Early detection of breast cancer in countries with limited resources. The Breast Journal, 9(s2).

[3]. Chandra, M.A., and Bedi, S.S., 2018. Survey on SVM and their application in Image Classification. International Journal of Information Technology, 13(5), pp.1–11.

[4]. Jian, W.,, Sun, X., and Luo, S., 2012. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform. BioMedical Engineering OnLine, 11(1).

[5]. Raghavendra, U.,, Rajendra Acharya, U.,, Fujita, H.,, Gudigar, A.,, Tan, J.H., and Chokkadi, S., 2016. Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Applied Soft Computing, 46, pp.151–161.

[6]. Nguyen, C.,, Wang, Y., and Nguyen, H.N., 2013. Random Forest classifier combined with feature selection for breast cancer diagnosis and prognostic. Journal of Biomedical Science and Engineering, 06(05), pp.551–560.

[7]. Yao, H.,, Zhang, X.,, Zhou, X., and Liu, S., 2019. Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification. Cancers, 11(12), p.1901.

[8]. Wu, E.,, Wu, K.,, Cox, D., and Lotter, W., 2018. Conditional infilling gans for data augmentation in mammogram classification. Image Analysis for Moving Organ, Breast, and Thoracic Images, pp.98–106.

[9]. Holste, G.,, Partridge, S.C.,, Rahbar, H.,, Biswas, D.,, Lee, C.I., and Alessio, A.M., 2021. End-to-end learning of fused image and non-image features for improved breast cancer classification from MRI. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

[10]. Sahu, A.,, Das, P.K., and Meher, S., 2023. High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomedical Signal Processing and Control, 80, p.104292.

[11]. Abdollahi, J.,, Davari, N.,, Panahi, Y., and Gardaneh, M., 2022. Detection of metastatic breast cancer from whole-slide pathology images using an ensemble deep-learning method. Archives of Breast Cancer, pp.364–376.

[12]. Sannasi Chakravarthy, S.R., and Rajaguru, H., 2022. Automatic detection and classification of mammograms using improved extreme learning machine with Deep Learning. IRBM, 43(1), pp.49–61.

[13]. Saber, A.,, Sakr, M.,, Abo-Seida, O.M.,, Keshk, A., and Chen, H., 2021. A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access, 9, pp.71194–71209.

[14]. Houssein, E.H.,, Emam, M.M., and Ali, A.A., 2022. An optimized deep learning architecture for breast cancer diagnosis based on improved Marine Predators algorithm. Neural Computing and Applications, 34(20), pp.18015–18033.

[15]. Man, R.,, Yang, P., and Xu, B., 2020. Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks. IEEE Access, 8, pp.155362–155377.

[16]. Ghiasi, M.M., and Zendehboudi, S., 2021. Application of decision tree-based ensemble learning in the classification of breast cancer. Computers in Biology and Medicine, 128, p.104089.

[17]. Zhang, Y.-D.,, Satapathy, S.C.,, Guttery, D.S.,, Górriz, J.M., and Wang, S.-H., 2021. Improved breast cancer classification through combining graph convolutional network and convolutional neural network. Information Processing & Management, 58(2), p.102439.

Cite this article

Zhang,Z. (2024). A comparison of recent progress in breast cancer diagnosis models using machine learning algorithms. Applied and Computational Engineering,39,103-113.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-303-6(Print) / 978-1-83558-304-3(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.39
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Anon, n.d. Who launches new roadmap on breast cancer. [online] World Health Organization. Available from: https://www.who.int/news/item/03-02-2023-who-launches-new-roadmap-on-breast-cancer [Accessed 31 Aug. 2023].

[2]. Anderson, B.O.,, Braun, S.,, Lim, S.,, Smith, R.A.,, Taplin, S., and Thomas, D.B., 2003. Early detection of breast cancer in countries with limited resources. The Breast Journal, 9(s2).

[3]. Chandra, M.A., and Bedi, S.S., 2018. Survey on SVM and their application in Image Classification. International Journal of Information Technology, 13(5), pp.1–11.

[4]. Jian, W.,, Sun, X., and Luo, S., 2012. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform. BioMedical Engineering OnLine, 11(1).

[5]. Raghavendra, U.,, Rajendra Acharya, U.,, Fujita, H.,, Gudigar, A.,, Tan, J.H., and Chokkadi, S., 2016. Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Applied Soft Computing, 46, pp.151–161.

[6]. Nguyen, C.,, Wang, Y., and Nguyen, H.N., 2013. Random Forest classifier combined with feature selection for breast cancer diagnosis and prognostic. Journal of Biomedical Science and Engineering, 06(05), pp.551–560.

[7]. Yao, H.,, Zhang, X.,, Zhou, X., and Liu, S., 2019. Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification. Cancers, 11(12), p.1901.

[8]. Wu, E.,, Wu, K.,, Cox, D., and Lotter, W., 2018. Conditional infilling gans for data augmentation in mammogram classification. Image Analysis for Moving Organ, Breast, and Thoracic Images, pp.98–106.

[9]. Holste, G.,, Partridge, S.C.,, Rahbar, H.,, Biswas, D.,, Lee, C.I., and Alessio, A.M., 2021. End-to-end learning of fused image and non-image features for improved breast cancer classification from MRI. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

[10]. Sahu, A.,, Das, P.K., and Meher, S., 2023. High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomedical Signal Processing and Control, 80, p.104292.

[11]. Abdollahi, J.,, Davari, N.,, Panahi, Y., and Gardaneh, M., 2022. Detection of metastatic breast cancer from whole-slide pathology images using an ensemble deep-learning method. Archives of Breast Cancer, pp.364–376.

[12]. Sannasi Chakravarthy, S.R., and Rajaguru, H., 2022. Automatic detection and classification of mammograms using improved extreme learning machine with Deep Learning. IRBM, 43(1), pp.49–61.

[13]. Saber, A.,, Sakr, M.,, Abo-Seida, O.M.,, Keshk, A., and Chen, H., 2021. A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access, 9, pp.71194–71209.

[14]. Houssein, E.H.,, Emam, M.M., and Ali, A.A., 2022. An optimized deep learning architecture for breast cancer diagnosis based on improved Marine Predators algorithm. Neural Computing and Applications, 34(20), pp.18015–18033.

[15]. Man, R.,, Yang, P., and Xu, B., 2020. Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks. IEEE Access, 8, pp.155362–155377.

[16]. Ghiasi, M.M., and Zendehboudi, S., 2021. Application of decision tree-based ensemble learning in the classification of breast cancer. Computers in Biology and Medicine, 128, p.104089.

[17]. Zhang, Y.-D.,, Satapathy, S.C.,, Guttery, D.S.,, Górriz, J.M., and Wang, S.-H., 2021. Improved breast cancer classification through combining graph convolutional network and convolutional neural network. Information Processing & Management, 58(2), p.102439.

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