Deep learning and transfer learning based breast cancer classification

CNN and transfer learning were used to classify breast cancer using deep learning. The suggested system has great diagnostic accuracy and clinical decision support capabilities. Integrating multimodal data like clinical and genomic features, using vision transformer architectures for feature extraction, federated learning for hospital privacy, and improving explainability for clinical adoption are future goals. .

[1] Y. Chen et al., “Deep Convolutional Networks for MammographyBased Breast Cancer Detection,” IEEE Access, 2023.   [2] M. Hassan et al., “Advanced CNN Architectures for Medical Image Classification,” IEEE Transactions on Artificial Intelligence, 2023.   [3] S. Kumar et al., “MultiScale Deep Learning Model for Breast Tumor Classification,” IEEE Journal of Biomedical and Health Informatics, 2024.   [4] L. Wang et al., “Transfer Learning for Histopathological Breast Cancer Image Classification,” IEEE Access, 2024.   [5] R. Patel et al., “Explainable AI for Breast Cancer Diagnosis Using Deep Learning,” IEEE Transactions on Neural Networks and Learning Systems, 2025.   [6] H. Lee et al., “Lightweight Deep Learning Models for Clinical Breast Cancer Detection,” IEEE Internet of Things Journal, 2025.   [7] A. Sharma et al., “Ensemble Deep Learning for Robust Breast Cancer Classification,” IEEE Access, 2026.   [8] T. Nguyen et al., “Efficient Medical Image Classification Using Deep Neural Networks,” IEEE Access, 2024.   [9] P. Singh et al., “Optimized CNN Architectures for Oncological Image Analysis,” IEEE Transactions on Artificial Intelligence, 2025.   [10] J. Brown et al., “AIBased Clinical Decision Support Systems in Oncology,” IEEE Access, 2026.