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@article{163893,
        author = {NETI PRAVEEN and Dr. Subhani Shaik and Dr. SVVSR Kumar Pullela},
        title = {Breast Cancer Histopathological Images Multi-classification using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2259-2266},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163893},
        abstract = {In recent years, breast cancer classification can be considered a primary subject for biology and health care, given that cancer is the second leading cause of death in women. From there, the medical community has seen advances in the field of research in the use of various techniques to screen for and identify multifold threatening diseases, such as breast cancer. In this survey, the various deep learning (DL) approaches are analyzed for multi-classifying the breast cancer histopathological images. This survey discusses the significant assumptions, limitations, and advantages are analyzed in existing DL based techniques as segmentation and classification are used for multi-classification of breast cancer histopathological images. The existing method’s performance was analyzed by using various performance measures such as accuracy, precision, recall, sensitivity, specificity and f1-score. This survey  concludes that the various breast cancer histopathological image multi-classification over DL have feasible to overcome the drawbacks as inefficient supervised feature and enhance the efficiency.},
        keywords = {Breast Cancer, Deep Features, Deep Learning, Histopathological image, Multi-classification and Segmentation},
        month = {},
        }