Abstract
Breast cancer is one among the foremost widely recognized kind of cancer among feminine population in the entire world. It is still challenging task to detect and classify the cancer tumor precisely. Mammography is considered as a standout amongst the most conclusive and dependable method for proper identification and classification of the breast cancer. Here, in this paper we are proposing a system based on machine learning for classification of breast cancer (BC) along with the comparative study of two machine learning (ML) classifier. The idea is to select the region of interest (ROI) at very first from the mammograms. At that point important features has been extracted using GLCM (grey level co-occurrence matrix). Thereafter, extracted features are then utilized to train our classifiers SVM and KNN individually. The mammogram are then characterized either into benign or malignant using the trained classifier. Proposed system is implemented on standard MIAS databases. Classification performance of both classifiers are contrasted in terms of accuracy, recall, precision, specificity and F1 score. We found that SVM achieved higher accuracy of 94% than KNN with better recall and F1 score.
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Pharswan, R., Singh, J. (2020). Performance Analysis of SVM and KNN in Breast Cancer Classification: A Survey. In: Balas, V., Solanki, V., Kumar, R. (eds) Internet of Things and Big Data Applications. Intelligent Systems Reference Library, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-030-39119-5_9
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DOI: https://doi.org/10.1007/978-3-030-39119-5_9
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