Copyright © 2026 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{195511,
        author = {Priyadarshini Y R and Priyanka K Devadiga and Ruchitha},
        title = {Breast Cancer Diagnosis Deep learning techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {154-158},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195511},
        abstract = {Breast cancer is one of the most prevalent forms of cancer and remains a leading cause of mortality among women worldwide. Early and reliable detection plays a vital role in improving patient outcomes, as timely diagnosis can significantly reduce risks and support more effective treatment planning. Traditional diagnostic methods often rely on manual evaluation, which may be influenced by subjectivity and limited by the complexity of medical data. With the growing availability of patient records and advancements in machine learning (ML), there is an increasing opportunity to develop automated systems that can assist clinicians in making accurate and consistent decisions.
In this work, we present an end-to-end machine learning framework aimed at improving both breast cancer diagnosis and prognosis. The proposed system integrates several classical ML algorithms including Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), and Random Forest (RF) to build a diverse ensemble. The predictions from these models are then stacked and passed into an Artificial Neural Network (ANN), which acts as a meta- learner. This layered strategy enhances the overall robustness and accuracy of the system, as it leverages the complementary strengths of individual models.},
        keywords = {},
        month = {March},
        }