Exploring the Role of Deep Learning in Image Analysis for Breast Cancer Diagnosis

Abstract

This study explores the use of deep learning, particularly convolutional neural networks (CNNs), for breast cancer diagnosis through image analysis. It highlights the importance of early detection for improving survival rates and the limitations of traditional methods like mammography and ultrasound, which often lack sufficient sensitivity. Deep learning allows automatic feature extraction from medical images, improving accuracy. The paper reviews CNN architectures used for classification and detection, noting their advantages over conventional techniques. Challenges include the need for large annotated datasets, high computational power, and lack of standardized protocols for AI-based diagnostics. It also discusses model evaluation metrics such as AUC, precision, and recall, and the potential for integrating explainable AI to enhance interpretability and support personalized medicine.