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@article{195125,
        author = {Thakur Harshini},
        title = {Breast Cancer Detection Using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6924-6930},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195125},
        abstract = {Over the past few years, advancements in artificial intelligence have significantly impacted the field of medical diagnostics, particularly in cancer detection. Among these, breast cancer remains one of the leading causes of mortality among women worldwide, making early and accurate diagnosis crucial for effective treatment. This study explores the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), for the detection and classification of breast cancer. Due to the complexity and variability in medical imaging, traditional diagnostic methods often face challenges in maintaining high levels of accuracy and consistency. By leveraging historical mammogram and histopathological image data, our research aims to enhance diagnostic precision through automated analysis. CNNs are well-suited for image-based tasks due to their ability to learn intricate spatial features and patterns. The proposed model demonstrates a strong capability to differentiate between benign and malignant tumors, achieving high performance metrics in terms of accuracy, sensitivity, and specificity. This research highlights the potential of CNN-based systems to support radiologists in early breast cancer detection, ultimately contributing to improved patient outcomes and reduced diagnostic errors.},
        keywords = {Breast cancer, detection, CNN, Convolutional Neural Network, medical images, diagnosis, tumor classification, benign, malignant, deep learning, radiologists, early detection, automated approach, precision.},
        month = {March},
        }