A Novel Approach to Predict the Early Stage of Breast Cancer Using Deep Learning

Abstract

Breast cancer is one of the leading causes of death among women, and its early detection plays a crucial role in improving the chances of successful treatment. Traditional diagnostic techniques such as mammography and biopsy are effective but often rely on manual interpretation, which can lead to misdiagnosis or delays. In this study, we propose a novel deep learning-based approach for the early prediction of breast cancer by integrating mammogram image analysis with Circulating Tumor Cell (CTC) counts. The aim is to develop an accurate and reliable model that not only identifies abnormal breast tissue but also correlates it with biological indicators of malignancy. The proposed approach combines the visual information obtained from mammography with biological insights derived from CTC enumeration, offering a comprehensive understanding of both structural and cellular-level changes. By leveraging the power of convolutional neural networks (CNNs) for feature extraction and data fusion techniques for multimodal integration, the model aims to provide a more reliable and non-invasive method for early diagnosis. This fusion of imaging and molecular data enhances clinical decision-making and represents a promising step toward precision-based cancer detection systems.

KEYWORDS: - Early-Stage Breast Cancer, CTC Biomarkers, Mammogram Analysis, CNN, Deep Learning