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- W2790753257 abstract "The contrast enhancement of mammograms at preprocessing stage optimizes the overall performance of a computer-aided detection (CAD) system for breast cancer. In the proposed approach, contrast enhancement is performed using a sigmoidal transformation mechanism followed by extracting a set of 14 Haralick features. For classification purposes, a support vector machine (SVM) classifier is used which sorts the input mammogram into either normal or abnormal subclasses. The performance of the classifier is estimated by calculating parameters like accuracy, specificity, and sensitivity. The performance of the proposed approach has been reported to be better in comparison to other existing approaches." @default.
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- W2790753257 date "2018-01-01" @default.
- W2790753257 modified "2023-10-03" @default.
- W2790753257 title "Haralick Features-Based Classification of Mammograms Using SVM" @default.
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- W2790753257 doi "https://doi.org/10.1007/978-981-10-7512-4_77" @default.
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