Abstract
Preprocessing and enhancement of mammograms is necessary to improve the visual quality and detectability of the anomalies present in the breasts. In this work, a sigmoidal transformation technique has been applied for enhancement (preprocessing) of mammograms. Gray-level co-occurrence matrix (GLCM) has been used for computation of textural features. Finally, Support Vector Machine (SVM) is used as a classification tool for sorting the mammogram into normal or abnormal. The accuracy, sensitivity, and specificity of the classifier are deployed as the performance parameters. The proposed approach has reported considerably better accuracy in comparison to other existing approaches.
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Bhateja, V., Tiwari, A., Gautam, A. (2018). Classification of Mammograms Using Sigmoidal Transformation and SVM. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 78. Springer, Singapore. https://doi.org/10.1007/978-981-10-5547-8_20
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DOI: https://doi.org/10.1007/978-981-10-5547-8_20
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