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@article{192706,
        author = {Alka Chouhan and Swati Khanve and Nitya Khare},
        title = {Study of Breast Cancer Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2110-2116},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192706},
        abstract = {Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide, where early and accurate diagnosis is crucial for improving patient outcomes. Magnetic Resonance Imaging (MRI) is a highly sensitive imaging modality for breast cancer detection; however, manual interpretation of MRI scans is time-consuming and prone to inter-observer variability. In recent years, machine learning (ML) and deep learning (DL) techniques have been extensively explored to automate breast cancer prediction and diagnosis using MRI images. This paper presents a comprehensive and structured review of MRI-based breast cancer diagnosis techniques employing machine learning approaches. The reviewed studies are systematically categorized into traditional machine learning methods, radiomics- based models, and deep learning approaches, including convolutional neural networks and transfer learning frameworks. A critical comparative analysis is performed by summarizing key quantitative outcomes such as accuracy, sensitivity, specificity, datasets, and evaluation metrics. The review highlights that deep learning models consistently outperform traditional machine learning techniques due to their ability to automatically extract discriminative features from MRI data. However, challenges such as limited dataset size, lack of standardized benchmarking, high computational complexity, and limited model interpretability remain significant barriers to clinical adoption.
Finally, this survey identifies existing research gaps and outlines future research directions, emphasizing the need for large-scale standardized MRI datasets, explainable artificial intelligence models, and robust evaluation frameworks. The findings of this review aim to assist researchers and clinicians in developing reliable and clinically applicable MRI- based breast cancer prediction systems.},
        keywords = {MRI Image, Machine Learning, Cancer},
        month = {February},
        }