Breast Cancer Detection using CNN

Abstract

Breast cancer stands as a leading cause of cancer- related fatalities worldwide. Assessing cancer accurately through eosin-stained images remains a complex task, often resulting in discrepancies among medical professionals while reaching a con- clusive diagnosis. To streamline this intricate process, Computer- Aided Diagnosis (CAD) systems present a promising avenue, aiming to reduce costs and enhance efficiency. Traditional clas- sification methods hinge on problem-specific feature extraction, rooted in domain knowledge. However, addressing the multitude of challenges posed by these feature-centric techniques has led to the emergence of deep learning methods as viable alternatives. Here, we propose a novel approach employing Convolutional Neural Networks (CNNs) for the classification of hematoxylin and eosin-stained breast biopsy images. Our method categorizes images into four distinct groups: normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma. Additionally, it per- forms a binary classification distinguishing carcinoma from non- carcinoma cases. The meticulously designed network architecture facilitates information extraction across multiple scales, encom- passing both individual nuclei and overall tissue organization. This design choice enables seamless integration of our proposed system with wholeslide histology images. Notably, our method achieves a commendable accuracy of 77.8four-class classification and demonstrates a high sensitivity of 95.6% in identifying cancer cases.