AI-Driven Workflow for Early Breast Cancer Detection Using ResNet50 and Histopathological Images

Early detection of breast cancer is critical for improving patient survival rates, and recent advances in artificial intelligence have paved the way for more accurate diagnostic tools. This study introduces an innovative AI-driven workflow that employs the ResNet50 convolutional neural network to analyze histopathological images for early breast cancer detection. The proposed method leverages transfer learning to extract robust features from complex image data, significantly reducing the need for extensive manual annotation. An extensive preprocessing pipeline, including image normalization and data augmentation, was implemented to enhance the model’s generalization capabilities. The workflow integrates segmentation and classification steps, enabling the rapid identification of malignant tissue patterns within histopathological slides. Comparative experiments on publicly available datasets demonstrated that our approach achieves high sensitivity and specificity, outperforming several traditional machine learning techniques. Furthermore, the system’s scalability and potential for integration into clinical environments suggest its viability as a decision-support tool for pathologists. By automating parts of the diagnostic process, the workflow not only reduces diagnostic time but also minimizes inter-observer variability, leading to more consistent outcomes. Limitations related to data diversity and potential integration challenges have been identified, providing clear directions for future research. Overall, this study contributes to the evolving field of medical imaging by offering a robust, efficient, and scalable framework for early breast cancer detection, thereby enhancing the prospects for timely and personalized treatment interventions.