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- W4385758936 abstract "Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages. However, early detection can significantly increase chances of survival and improves the lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to provide comprehensive framework encompassing early detection, classification, and diagnosis. The artificial intelligence research community, in coordination with medical practitioners, is developing such frameworks to automate the task of detection. With the surge in research activities coupled with the availability of large datasets and enhanced computational powers, it is expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for the classification of breast cancer using mammograms is proposed. The proposed framework not only uses features extracted from Convolutional Neural Network (CNN) but also combines these features with handcrafted features (Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP)) which helps in embedding domain expert knowledge, a step towards making proposed framework transparent. Experimental results conducted on the CBIS-DDSM dataset demonstrate that the proposed framework outperforms the current state-of-the-art methods in breast cancer classification." @default.
- W4385758936 created "2023-08-12" @default.
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- W4385758936 date "2023-09-01" @default.
- W4385758936 modified "2023-09-23" @default.
- W4385758936 title "Breast cancer classification using deep learned features boosted with handcrafted features" @default.
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- W4385758936 doi "https://doi.org/10.1016/j.bspc.2023.105353" @default.
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