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Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies

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Abstract

Background

Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer.

Purpose

In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks.

Conclusion

Our review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.

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  1. Department of Biology, School of Sciences, Razi University, Baq-e-Abrisham, Kermanshah, 6714967346, Islamic Republic of Iran

    Mehran Radak, Haider Yabr Lafta & Hossein Fallahi

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  1. Mehran Radak
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MR and HF conceived the idea. HF supervised the procedure and verified the results and discussion. HL wrote the first draft, and HF edited the manuscript. MR and HF prepared the final draft.

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Correspondence to Hossein Fallahi.

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Radak, M., Lafta, H.Y. & Fallahi, H. Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies. J Cancer Res Clin Oncol 149, 10473–10491 (2023). https://doi.org/10.1007/s00432-023-04956-z

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