Deep Learning for Early Breast Cancer Detection and Personalized Treatment Strategies

Abstract

Breast cancer remains one of the most prevalent malignancies worldwide, where early diagnosis significantly improves survival rates and treatment outcomes. Recent advances in artificial intelligence (AI) and deep learning have demonstrated substantial potential to enhance histopathological image analysis and support precision oncology. This study presents an extended deep learning framework for early breast cancer detection using histopathological images and explores its potential application in personalized treatment strategies. The publicly available IDC_regular dataset comprising 277,524 image patches extracted from 162 whole-slide breast cancer specimens was utilized. A Convolutional Neural Network (CNN)-based architecture was employed for feature extraction and binary classification of invasive ductal carcinoma (IDC) positive and negative cases. The proposed framework incorporated image pre-processing, OpenCV-based transformations, data normalization, model training, and evaluation using precision, recall, F1-score, and accuracy metrics. Experimental results achieved an overall classification accuracy of 92%, with precision and recall values demonstrating reliable detection performance. Furthermore, saliency mapping techniques were introduced to improve interpretability and localize diagnostically relevant regions. The extracted deep features provide a foundation for future multi-class tumour characterization, risk stratification, and treatment response prediction. The findings suggest that AI-assisted pathology can reduce diagnostic workload, improve detection efficiency, and support personalized clinical decision-making. Future work will focus on integrating clinical, genomic, and treatment datasets to develop comprehensive precision oncology systems.