Evolution of Machine Learning Methods for Memography Classification
DOI:
https://doi.org/10.26438/ijcse/v6i3.499502Keywords:
Deep learning, Machine Learning, Revisiting SVM, SVMAbstract
In Healthcare and Biomedical sectors, the data is growing more and more, analysing of such medical data accurately will benefits disease detection and early diagnosis. Mammography is the process toward utilizing low-energy X-rays to look at the human cancer for diagnosis and screening. The objective of mammography is the early detection of breast cancer , ordinarily through recognition of trademark masses or macrocalcifications. Low positive predictive model of mammogram will lead to more no unnecessary biopsies with benign outcomes. The accuracy and reliability of prediction mechanisms is important to reduce the number of biopsies. In this paper, we look at different machine learning algorithms with a specific end goal to predict the performance accuracy. By comparing different algorithms, it has been concluded that deep learning algorithm and Revisiting SVM have highest prediction accuracy among other algorithms studied. Experimental results show this prediction approach is more effective.
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