Predictive Models for Early Detection of Breast Cancer Using Imaging Techniques

EasyChair Preprint 13569

Abstract

Breast cancer is a significant global health concern, and early detection plays a critical role in improving patient outcomes. Imaging techniques, such as mammography, magnetic resonance imaging (MRI), and ultrasound, have been widely used for breast cancer detection. However, the interpretation of imaging results can be challenging, leading to the potential for missed diagnoses or unnecessary interventions. Predictive models offer a promising approach to enhance early detection by leveraging machine learning and deep learning algorithms.

This abstract provides an overview of predictive models for early detection of breast cancer using imaging techniques. It explores the various imaging modalities commonly employed in breast cancer detection, including mammography, MRI, and ultrasound. Additionally, it examines the potential of emerging imaging techniques, such as tomosynthesis and molecular imaging, in improving early detection.

Keyphrases: Breast Cancer Detection, Mammography, data collection, diagnosis digital, imaging technology

@booklet{EasyChair:13569,
  author    = {Godwin Olaoye and Harold Jonathan},
  title     = {Predictive Models for Early Detection of Breast Cancer Using Imaging Techniques},
  howpublished = {EasyChair Preprint 13569},
  year      = {EasyChair, 2024}}