A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives

Abstract

Purpose: Breast cancer remains a leading cause of mortality in women worldwide, with notable disparities in incidence and prognosis across regions. This systematic review explores the application of Deep Learning-based computer-aided diagnostic (CAD) systems for breast cancer detection, with a special focus on Asia to highlight underrepresented perspectives and challenges.

Methods: We conducted a systematic Literature review in accordance with PRISMA guidelines. A comprehensive search of Scopus and Web of Science databases was performed to identify relevant studies published between January 2018 and November 2023, with an additional hand search for recent studies from 2024 to 2025. After screening 1051 records, 287 articles were included based on predefined inclusion and exclusion criteria. Quality assessment focused on the relevance of deep learning-based approaches to mammographic breast cancer detection, emphasizing global research trends and focused analysis of studies involving Asian populations.

Results: The review identified major research trends in deep learning-based mammographic analysis, with most studies focusing on lesion classification while comparatively fewer addressed detection, segmentation, and breast density assessment. Studies using Asian datasets revealed unique challenges, including higher breast density, limited annotations, and under-representation in public datasets. Analysis of methodologies highlighted varied use of image preprocessing and augmentation techniques. Focus maps were used to visualize contributions across tasks and populations, revealing gaps in multi-class BI-RADS classification and a global research bias toward Caucasian datasets (> 80%).

Conclusion: This review reveals that most deep learning models for breast cancer detection are trained predominantly on Caucasian datasets, creating significant limitations when applied to other populations due to demographic differences in breast density and imaging characteristics. To improve breast cancer screening globally, researchers must develop deep learning systems using diverse datasets that represent different populations, validate these models across various ethnic groups, and ensure clinical testing includes women from multiple demographic backgrounds.

Systematic review registration: PROSPERO CRD 42,023,478,896.

Keywords: Artificial intelligence; Asian; Breast cancer; Deep learning; Global trend; Mammograms; Mammography; Systematic literature review.

Fig. 1

Estimated incidence and deaths from 2022 to 2040 [2]

Fig. 2

Various elements of the Digital Mammogram image belonging to Vin-Dr Mammo dataset [15]

Fig. 3

PRISMA flowchart of the proposed SLR

Fig. 4

Publication Trend Over Five Years globally (left) and Publisher-wise contribution in publishing the articles over 5 years globally (right). Note: The 2024 entry (1 article) represents an article included based on early access criteria. SSRG (Seventh Sense Research Group), AIP (American Institute of Physics), Tech Science Press (Tech Science Press)

Fig. 5

Visualization of Co-Author citation with weights based on the number of articles worldwide (left) and for Asian countries (right)

Fig. 6

Visualization of country-wise citations with weights based on number of citations (left) and number of articles contributed (right)

Fig. 7

Visualization of frequent Keywords in the research field considered worldwide

Fig. 8

a Research Focus Map for Breast Lesions (Mass + Calcification) (b) Research Focus Map for Breast Density

Fig. 9

a Contribution Percentage based on Lesion and Density (b) Research Type Percentage based on Lesion and Density

Fig. 10

Frequently used Mammogram datasets

Fig. 11

As per Globocan 2022, plot showing Incidence (left) and Mortality (right) rates of the Asian population due to Female BC in 2022 [2]

Fig. 12

Augmentation Techniques used in the Literature for global datasets (left) and Asian datasets (right)