Abstract
Breast cancer is one of the most life frightening diseases in women. It arises due to the uncontrolled growth of cells in the breast. The area suffering from damage is known as a lesion that is classified as Benign and Malignant. This paper classifies the breast lesions using a ratio texture feature obtained from the texture features calculated inside the lesion (IAI) and the texture feature calculated on the upper side of the lesion (UAI). Statistical texture features like EDGE, SFM, NGTDM, FOS, GLCM, GLRLM, and GLDS are calculated. The SVM classifier is used to classify the lesions on the basis of ratio texture feature. The texture features calculated from IAI gains an overall accuracy of 62.2% with NGTDM texture feature whereas an overall accuracy of 82.2% is achieved in UAI using the GLCM texture feature. However, an overall accuracy of 86.6% is yielded with the FOS ratio texture vector having individual accuracies of 82% and 92.2% for benign and malignant class, respectively.
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Jain, S., Virmani, J. (2020). Texture Ratio Vector Technique for the Classification of Breast Lesions Using SVM. In: Jain, S., Paul, S. (eds) Recent Trends in Image and Signal Processing in Computer Vision. Advances in Intelligent Systems and Computing, vol 1124. Springer, Singapore. https://doi.org/10.1007/978-981-15-2740-1_14
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DOI: https://doi.org/10.1007/978-981-15-2740-1_14
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