Breast cancer is the most common cancer among women worldwide, and early detection is essential for improving survival rates. However, traditional manual diagnosis of breast cancer from histopathological images is time-consuming and subjective. This paper proposes a transfer learning-based approach for binary classification of benign and malignant cells in histopathological images. We use a pre-trained model and fine-tune it on the BreaKHis dataset, which contains histopathological images with manually attenuated benign and malignant cells. Our results suggest that transfer learning is a promising approach for developing accurate and efficient breast cancer detection models. Early detection of breast cancer can lead to better treatment outcomes and improved survival rates.