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@article{160949,
        author = {Vandana Lingampally and Dr. K. Radhika},
        title = {Reducing False Negatives in Breast Cancer Detection by combining Super-Resolution Images with Enhancement Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {2},
        pages = {180-199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160949},
        abstract = {In an effort to enhance the accuracy of breast cancer diagnosis, the proposed model utilizes super-resolution (SR) images to mitigate the incidence of false negatives. A super resolution image have more pixel density and thereby gives more detailed information about the abnormalities present in the mammogram which will help in deciding whether the abnormality is benign or malignant. To obtain high resolution images FSRCNN and LapSRN models were trained with CBIS-DDSM dataset. High resolution images were generated from trained FSRCNN and LapSRN models. These SR images were subsequently applied to a suite of deep learning models, and the findings indicate that the incorporation of SR images resulted in a significant improvement in the reduction of false negatives.},
        keywords = {Breast cancer, Super resolution, Deep learning, FSRCNN, LapSRN.},
        month = {},
        }