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• W3201361504 abstract "Breast cancer is one of the major causes of death among women worldwide. Efficient diagnosis of breast cancer in the early phases can reduce the associated morbidity and mortality and can provide a higher probability of full recovery. Computer-aided detection systems use computer technologies to detect abnormalities in clinical images which can assist medical professionals in a faster and more accurate diagnosis. In this paper, we propose a modified residual neural network-based method for breast cancer detection using histopathology images. The proposed approach provides good performance over varying magnification factors of 40X, 100X, 200X and 400X. The network obtains an average classification accuracy of 99.75%, precision of 99.18% and recall of 99.37% on BreakHis dataset with 40X magnification factor. The proposed work outperforms the existing methods and delivers state-of-the-art results on the benchmark breast cancer dataset." @default.
• W3201361504 created "2021-09-27" @default.
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• W3201361504 date "2021-10-01" @default.
• W3201361504 modified "2023-10-03" @default.
• W3201361504 title "Breast cancer detection from histopathology images using modified residual neural networks" @default.
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• W3201361504 doi "https://doi.org/10.1016/j.bbe.2021.08.011" @default.
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