MULTI-STAGE BREAST CANCER CLASSIFICATION FROM HISTO-PATHOLOGICAL IMAGES USING XCEPTIONNET

Abstract

Breast cancer is one of the leading causes of mortality among women worldwide, where early and accurate diagnosis plays a critical role in improving patient survival. In this study, a deep learning-based framework is proposed for the automated classification of breast cancer histopathological images. The proposed approach utilizes transfer learning with the XceptionNet architecture to perform both binary classification (benign vs. malignant) and multi-class classification of tumor subtypes. The model is trained and evaluated on the BreakHis dataset, which contains microscopic images at multiple magnification levels. Comprehensive preprocessing techniques, including image resizing, normalization, and data augmentation, are applied to improve model generalization. The dataset is divided into training, validation, and testing sets, and 5-fold cross-validation is employed to ensure robustness and reproducibility. The proposed model achieves superior performance with an accuracy of 98.57% for binary classification, while achieving competitive results of 95.4% and 93.82% for benign and malignant multi-class classification, respectively. In addition to accuracy, other evaluation metrics such as precision, recall, and F1-score are used to validate the effectiveness of the model. Experimental results demonstrate that the proposed framework outperforms several existing approaches in terms of classification accuracy and reliability. This study highlights the potential of deep learning and transfer learning techniques in assisting medical professionals for accurate and efficient breast cancer diagnosis.