Breast Cancer remains a critical global health issue, underscoring the importance of early detection for improved patient prognosis. This paper presents pilot studies employing Deep Learning techniques particularly Convolutional Neural Networks (CNN) to classify Breast Cancer histopathological images from the publicly available BC Histopathological Image Classification (BreaKHis) dataset. The work aims to enhance the robustness and generalization of the model by focusing on CNN models and leveraging Data Augmentation (DA) techniques such as rotation, flipping, and scaling the training dataset. The CNN model trained on the augmented dataset demonstrates promising sensitivity and accuracy in Breast Cancer detection. The proposed work not only advances Deep Learning based Breast Cancer detection but also underscores the significance of DA in mitigating the scarcity of medical images for training. Ultimately, these findings have implications for early Breast Cancer diagnosis, potentially improving patient outcomes and saving lives.