Advances in Applied Nano-Bio Technologies

Published date: Mar 20 2025

Journal Title: Advances in Applied Nano-Bio Technologies

Issue title: Advances in Applied Nano-Bio Technologies: Volume 6 Issue 1

Pages: 24 - 45

DOI: 10.18502/aanbt.v6i1.18227

Mohadeseh Parhizkarist_m_parhizkari@azad.ac.irDepartment of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran

Mahdi Shabanzadehmopa2025.2@gmail.comDepartment of Biomedical Engineering, Science and Research Branch, Islamic Azad University

Fatemeh AbbasiChemistry Department, College of Sciences, Shiraz University, Shiraz 71946-84795

Sadra SaremiDepartment of Physics, Iran University of Science and Technology, Tehran 16846-13114

Breast cancer (BC) continues to be a major global health concern, with rising incidence rates each year. Timely identification is essential for enhancing patient outcomes, but conventional diagnostic techniques often fall short in terms of precision and effectiveness. This review explores the role of artificial intelligence (AI) and machine learning in transforming BC detection, with a focus on advancements up to 2024. A thorough review of recent studies was conducted, emphasizing the application of machine learning in BC detection across diverse data sources, including microarray data, medical imaging such as mammography, ultrasound, (Magnetic Resonance Imaging) (MRI), and histopathology, and clinical records. The analysis traces the progression from traditional machine learning methods to sophisticated deep learning frameworks, especially convolutional neural networks (CNNs), and assesses their effectiveness in real-world clinical environments. Advances in AI have led to notable gains in diagnostic accuracy, with deep learning models delivering exceptional performance in experimental studies. Hybrid imaging strategies that integrate multiple imaging modalities with AI algorithms have proven particularly effective, especially in detecting abnormalities in dense breast tissue. Innovations like transfer learning and explainable AI have enhanced the adaptability and transparency of these models. Nevertheless, issues related to data quality, computational demands, and the lack of standardized protocols remain unresolved. Although AI-driven detection systems exhibit considerable potential in research contexts, their broader adoption in clinical practice faces several hurdles. Future progress will depend on overcoming challenges such as data standardization, improving model interpretability, and optimizing computational efficiency. Combining AI technologies with established diagnostic practices offers a promising approach to advancing the accuracy and accessibility of BC detection.

Keywords: breast cancer detection, machine learning, deep learning, computer-aided detection

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