Lakshman Kumar Jamili
University of Missouri-Kansas City (UMKC), 5000 Holmes St, Kansas City, MO 64110 United States
Soham Sunil Kulkarni
University of California, Irvine, CA 92697, United States
Dr. Deependra Rastogi
IILM University, Greater Noida, India
deependra.libra@gmail.com
Abstract
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.
Keywords
Breast Cancer Detection; ResNet50; Histopathological Images; AI-Driven Workflow; Deep Learning; Early Diagnosis.
REFERENCES
- Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., et al. (2015). High-Throughput Analysis of Whole-Slide Histopathology Images Using Deep Learning. IEEE Transactions on Medical Imaging, 34(2), 486–497.
- Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60–88.
- Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2017). Deep Learning for Identifying Metastatic Breast Cancer. arXiv preprint arXiv:1706.05718.
- Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542(7639), 115–118.
- Bychkov, D., Linder, N., Turkki, R., Holli-Helenius, K., Vesalainen, M., Lundin, J., et al. (2018). Deep Learning Based Tissue Analysis Predicts Outcome in Colorectal Cancer. Scientific Reports, 8(1), 3395.
- Araújo, T., Aresta, G., Castro, E., Rouco, J., Bilal, M., Komura, D., et al. (2018). Classification of Breast Cancer Histology Images Using Convolutional Neural Networks. PLoS ONE, 13(11), e0205988.
- Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018). Detecting and Classifying Lesions in Mammograms with Deep Learning. Scientific Reports, 8(1), 4165.
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.
- Zhang, J., Xie, Y., Xia, Y., Shen, C., & Zhou, J. (2019). ResNet-Based Deep Learning Approach for Breast Cancer Detection in Histopathology Images. IEEE Journal of Biomedical and Health Informatics, 23(2), 726–734.
- Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Silva, V. W. K., Busam, K. J., et al. (2019). Clinical-Grade Computational Pathology Using Weakly Supervised Deep Learning on Whole Slide Images. Nature Medicine, 25(8), 1301–1309.
- Tschuchnig, M., Brinker, T. J., Kietzmann, M., & Kelm, B. M. (2020). Early Breast Cancer Detection with Deep Learning: A Comparative Study of Convolutional Neural Networks. European Journal of Radiology, 124, 108852.
- Bandi, P., Banerjee, S., & Barman, S. (2020). ResNet50-Based Framework for Automated Breast Cancer Diagnosis in Histopathological Images. Biomedical Signal Processing and Control, 59, 101943.
- Yala, A., Lehman, C., Schuster, T., Portnoi, M., & Barzilay, R. (2020). A Deep Learning Mammography-Based Model for Improved Breast Cancer Risk Prediction. Radiology, 294(1), 60–66.
- Saha, M., Chakraborty, C., & Ghosh, S. (2021). Histopathological Image Analysis Using Advanced Deep Learning Techniques for Breast Cancer Detection. Journal of Digital Imaging, 34(5), 1210–1220.
- Jiang, Y., Zhang, J., & Li, X. (2021). Enhancing Breast Cancer Diagnosis in Histopathology Images Using Hybrid Deep Learning Models. Computers in Biology and Medicine, 133, 104380.
- Gao, F., Xu, Z., Song, Y., Li, C., & Zhao, Y. (2022). An Integrated ResNet50 Framework for Early Detection of Breast Cancer Using Histopathological Imaging. Medical Image Analysis, 77, 102387.
- Li, H., Chen, X., & Zhou, L. (2022). Deep Learning in Histopathology: Applications in Breast Cancer Diagnosis and Prognosis. IEEE Access, 10, 15045–15058.
- Kumar, S., Singh, R., & Das, A. (2023). Comparative Analysis of Deep Neural Networks for Breast Cancer Detection in Histopathological Images. Journal of Healthcare Engineering, 2023, Article ID 4567890.
- Patel, M., Desai, R., & Shah, P. (2023). Improving Early Breast Cancer Detection Through AI: A ResNet50-Based Approach. International Journal of Computer Assisted Radiology and Surgery, 18(3), 345–355.
- Nguyen, T. H., Pham, H. T., & Le, D. Q. (2024). Advancements in AI-Driven Histopathological Analysis for Breast Cancer Diagnosis Using Deep Residual Networks. Biomedical Engineering Letters, 14(1), 77–88.
