Onco Breast Cancer Detection System: Explainable Deep Learning Framework for Ultrasound-Based Breast Cancer Diagnosis

References

[1] World Health Organization, Global Cancer Observatory: Cancer Today, International Agency for Research on Cancer, 2023. Available: https://gco.iarc.fr/today

[2] D. Han, Y. Kim, and S. Park, “Deep Learning-Based Breast Cancer Detection Using Ultrasound Images,” IEEE Access, vol. 9, pp. 114622–114634, 2021. doi: https://doi.org/10.1109/ACCESS.2021.3104567

[3] H. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of Breast Ultrasound Images (BUSI) for Breast Cancer Detection,” Data in Brief, vol. 28, p. 104863, 2020. doi: https://doi.org/10.1016/j.dib.2019.104863

[4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. doi: https://doi.org/10.1109/CVPR.2016.90

[5] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in Proc. Int. Conf. Learning Representations (ICLR), 2015. Available: https://arxiv.org/abs/1409.1556

[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017. doi: https://doi.org/10.1145/3065386

[7] R. Yamashita, M. Nishio, R. K. Do, and K. Togashi, “Convolutional Neural Networks: An Overview and Application in Radiology,” Insights into Imaging, vol. 9, pp. 611–629, 2018. doi: https://doi.org/10.1007/s13244-018

[8] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson Education, 2018.

[9] F. Chollet, Deep Learning with Python, Manning Publications, 2018. Available: https://www.manning.com/books/deep-learning- with-python

[10] R. Selvaraju et al., “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” in Proc. IEEE Int. Conf. Computer Vision (ICCV), 2017, pp. 618–626. doi: https://doi.org/10.1109/ICCV.2017.74

[11] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. Available: https://www.deeplearningbook.org

[12] A. Esteva et al., “A Guide to Deep Learning in Healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019. doi:

[13] J. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017. doi: https://doi.org/10.1016/j.media.2017.07.005

[14] T. Shen, J. Li, and H. Zhang, “Breast Tumor Classification in Ultrasound Images Using Deep Convolutional Neural Networks,” Biomedical Signal Processing and Control, vol. 71, p. 103086, 2022. doi:

[15] M. Abadi et al., “TensorFlow: A System for Large- Scale Machine Learning,” in Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016. Available: https://www.usenix.org/conference/osdi1

@article{1715200,
      author = {Mopuru Upendra Reddy, Mounika N, Niveditha A, Nayana C, Prof. Rajani Kodagali},
      title = {Onco Breast Cancer Detection System: Explainable Deep Learning Framework for Ultrasound-Based Breast Cancer Diagnosis},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2963-2970},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715200.pdf},
      abstract = {Breast cancer remains one of the most common and life-threatening diseases affecting women worldwide. Early detection significantly increases survival rates and improves treatment outcomes. However, manual diagnosis using ultrasound images requires experienced radiologists and may lead to diagnostic variability. This paper proposes an AI-driven breast cancer detection system that combines deep learning classification with explainable artificial intelligence techniques to assist medical professionals in early diagnosis. The proposed system utilizes a Convolutional Neural Network (CNN) trained on the BUSI Breast Ultrasound dataset to classify images into three categories: benign, malignant, and normal. To improve model transparency, Grad-CAM explainability is integrated to visualize the regions of ultrasound images that influence the model's prediction. A real-time Streamlit dashboard enables clinicians to upload ultrasound images, obtain predictions, visualize heatmaps, and generate downloadable diagnostic reports. Experimental results show that the proposed model achieves high classification accuracy and strong performance across evaluation metrics including precision, recall, and F1-score. Grad-CAM explanations provide interpretable insights that improve trust in AI-assisted diagnosis. The system demonstrates how combining deep learning with explainable AI can support clinicians in early breast cancer detection and improve healthcare decision- making.},
      keywords = {Breast Cancer Detection, Deep Learning, CNN, Explainable AI, Grad-CAM, Ultrasound Imaging, Medical Image Analysis, BUSI Dataset, AI-assisted Diagnosis, Healthcare Decision Support.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715200}
  }