Paper Title
AI-Driven Histopathological Image Analysis for Breast Cancer Diagnosis: HistoBreast

Abstract
Breast cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for early and accurate diagnosis. Histopathological imaging plays a crucial role in identifying can- cerous tissues by analyzing stained biopsy samples. In this study, we propose an AI-driven deep learning framework for automated breast cancer classification using histopathological images. Our approach in- tegrates transfer learning with pre-trained CNN architectures such as VGG16, ResNet50, and Efficient- Net to enhance classification accuracy. To improve model performance, we apply data augmentation techniques and leverage Gradient-weighted Class Activation Mapping (Grad-CAM) for explainability. Experimental evaluation on the Breast Histopathology Images dataset demonstrates that EfficientNet achieves the highest classification accuracy, with an F1-score of 0.8915, making it the most effective model for distinguishing between malignant and benign cases. The proposed system is deployed as an interactive Streamlit-based AI application, providing a user-friendly interface for medical professionals. This research contributes to advancing AI-powered histopathological analysis, facilitating faster and more reliable breast cancer diagnosis. Keywords - Breast Cancer Classification, Deep Learning, Hybrid Integrated Model, Feature Pyra- mid Network (FPN), Channel Attention Mechanism