Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{179875,
        author = {Pranav Soni and Rohini Sharma and Manu kumar and Yash Sonkar and Brijesh kumar},
        title = {CancerGuard: Predictive Modeling for Breast Cancer Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {9162-9166},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179875},
        abstract = {Mammography images can effectively be used to detect breast cancer, which remain a leading cause of mortality among women worldwide. Cancer Guard: Predictive Modelling for Breast Cancer created a framework that combines YOLO and a customized UaNet through deep learning to achieve this goal. Our Cancer Guard system improved the accuracy of detection significantly compared to conventional methods because our model utilized three generalizable datasets – In Breast, CBIS-DDSM, and MIAS. The deployed framework achieves an end-to-end pipeline, including dataset preprocessing, model training, evaluation, and web-based real-time diagnosis. CancerGuard demonstrates greater accuracy in localization and classification of lesions through a dual- model strategy. We were able to successfully place CancerGuard at the forefront of innovation for AI driven breast cancer detection by significantly improving precision and accuracy.},
        keywords = {},
        month = {June},
        }