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Review
. 2024 May 9;10(5):705-726.
doi: 10.3390/tomography10050055.

A Review of Artificial Intelligence in Breast Imaging

Dhurgham Al-Karawi  1 , Shakir Al-Zaidi  1 , Khaled Ahmad Helael  2 , Naser Obeidat  3 , Abdulmajeed Mounzer Mouhsen  3 , Tarek Ajam  3 , Bashar A Alshalabi  3 , Mohamed Salman  3 , Mohammed H Ahmed  4
Affiliations
Review

A Review of Artificial Intelligence in Breast Imaging

Dhurgham Al-Karawi et al. Tomography. .

Abstract

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Keywords: artificial intelligence network; breast cancer; deep learning; machine learning; mammography image; ultrasound image.

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Conflict of interest statement

Author Dhurgham Al-Karawi and Shakir Al-Zaidi was employed by the company Medical Analytica Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of ML for breast cancer.
Figure 2
Figure 2
(a) A sample from the DDSM dataset [26], and (b) a sample of breast ultrasound images from the dataset [34].
Figure 3
Figure 3
Proposed method for breast cancer segmentation from thermography images [88].
Figure 4
Figure 4
ENAS-B framework proposed in [96] for automatically designing a CNN model for breast cancer classification from ultrasound images.
See this image and copyright information in PMC

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