Deep Learning in Medical Imaging: Chronological Evolution, Frameworks, Core Methods, and Recent Advances in Breast Cancer Segmentation (2023–2024)

Medical imaging plays a crucial role in modern healthcare, facilitating the diagnosis and treatment of various diseases. The advent of deep learning has revolutionized the processing and analysis of medical images. This paper reviews recent literature on deep learning applications in medical imaging, focusing specifically on segmentation and classification for disease diagnosis and treatment. We discuss recent advancements in deep learning architectures tailored for these tasks, highlighting their relevance and effectiveness. The studies reviewed span the period from January 2023 to April 2024, concentrating on the latest deep learning methods proposed for breast cancer segmentation. Additionally, we explore the availability and characteristics of publicly available medical image datasets for breast cancer, emphasizing their importance in training and evaluating deep learning models. An overview of commonly used metrics for assessing model efficacy is provided, underscoring their role in quantifying performance. Furthermore, we address the challenges and limitations faced by deep learning methods in medical imaging. Through analysis and discussion, we propose innovative directions to address these challenges, paving the way for promising future applications in early disease detection and personalized treatment planning.