Breast Cancer Histopathology Image Analysis Using Deep Learning
Shweta R. Patil, R. K. Yadav · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025
Globally, breast cancer is one of the main causes of death for women. Improved survival rates and successful treatment depend on early and precise identification. Although manual analysis of histopathological pictures is time-consuming and subject to variation among pathologists, these images offer crucial diagnostic information. Using histopathology pictures, we provide a deep learning-based method in this article to automatically detect breast cancer. To identify benign and malignant instances, we use Convolutional Neural Networks (CNNs) to extract characteristics. The suggested model outperforms more established machine learning methods like Support Vector Machines (SVM) in terms of accuracy after being trained on the Break His dataset. According to our findings, deep learning can greatly improve the precision and effectiveness of breast cancer diagnosis. Keywords: Convolutional Neural Networks, Histopathological Pictures, Deep Learning, Breast Cancer, Break His Dataset, SVM