Medical Imaging Analysis for Breast Cancer Using Deep Learning

Breast cancer remains one of the most prevalent and deadly forms of cancer among women worldwide. Early detection plays a pivotal role in improving patient outcomes, as it enables timely intervention and treatment. In recent years, advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have revolutionized medical imaging and diagnostic processes. This project proposes a novel approach for breast cancer detection leveraging CNNs and deep learning concepts. The primary objective of this project is to develop a robust and accurate system for automated breast cancer detection using CNNs trained on mammography images. The proposed system aims to capitalize on the inherent ability of CNNs to extract complex features and patterns from medical images, enabling accurate identification of cancerous lesions. Through the integration of deep learning concepts, including transfer learning and data augmentation, the system seeks to enhance its performance and generalization ability across diverse patient populations. The project methodology involves collecting a dataset of mammography images annotated with corresponding breast cancer labels. Preprocessing techniques, such as normalization and augmentation, are applied to enhance the quality and diversity of the dataset. Subsequently, CNN architectures are designed and trained on the dataset, with an emphasis on optimizing performance metrics such as accuracy, sensitivity, and specificity.