ItemConference Paper

Advancements and challenges in deep learning for breast cancer screening: A review

Title
Advancements and challenges in deep learning for breast cancer screening: A review
Creator
Debnath, Rimpi; Paul, Jeno Lovesum Selvaraj; Thiyagarajan, Vigneshwaran
Description
Breast cancer continues to be the prevalent cancer on a global scale, playing a major role in the worldwide cancer statistics, the critical role of early detection in reducing death rates is underscored. In the context of breast cancer, screening, deep learning (DL) emerged as a game-changer, providing notable improvements over existing techniques. This review explores the use of DL in analysing images from various sources such as X-rays, ultrasound, magnetic resonance imaging, and biopsies. Additionally, it highlights DL's potential to pre-screen for cancer by integrating diverse data, including demographic information, biological markers, and meta-analytical risk assessments. The analysis reveals that deep learning frameworks, especially those optimized with feature selection techniques, attain the minimal false-negative rates, effectively distinguishing between patients with and without cancer. Notably, DL models demonstrate lower prediction uncertainty compared to traditional machine learning, as shown by reduced standard deviations in performance metrics. Additionally, the paper proposes a cascade network model that achieves 98.61% classification accuracy and a 98.41% F1 score by segmenting tumours with a UNet architecture and classifying them with a ResNet backbone. Despite these advancements, challenges such as limited annotated data and adaptability to new data domains persist. In response to these issues, the proposed Self AdaptNet leverages innovative self-supervised learning and adversarial techniques to improve the resilience as well as adaptability of BC detection models.AI technology, particularly DL-based systems, has the capacity to completely transform breast cancer screening by improving screening accuracy and reducing observer variability. However, clinical adoption requires standardized guidelines, trustworthy AI practices, and collaboration among researchers, clinicians, and regulatory bodies. 2026 Author(s).
Source
AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20267;
Date
01-01-2026
Publisher
American Institute of Physics
Subject
Breast cancer screening; Deep learning (DL); Feature selection; Self-supervised learning; UNet architecture
Coverage
Debnath R., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Paul J.L.S., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Thiyagarajan V., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 0094243X;
Format
online
Language
English
Type
Conference paper

Citation

Debnath, Rimpi; Paul, Jeno Lovesum Selvaraj; Thiyagarajan, Vigneshwaran, “Advancements and challenges in deep learning for breast cancer screening: A review,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25724.

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