Abstract
With the surge of breast cancer, researchers have proposed many predicting methods and techniques. Currently, mammograms and analyzing the biopsy images are the two traditional methods used to detect the breast cancer. In this paper, the objective is to create a model that can classify or predict whether breast cancer is benign or malignant. Typically, a pathologist will take several days to analyze a biopsy, while the model can analyze thousands of biopsies in few seconds. For the numerical data, various machine learning classifications with supervised learning algorithms such as random forest (RF), K-nearest neighbor (KNN), Naïve Bayes, support vector machines (SVM), and decision trees (DT) are used. Then, deep learning—convolutional neural network is used to analyze the biopsy images from a dataset of images. An accurate result from the prediction are determined for saving the lives of people.
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Thangavel, M., Patnaik, R., Mishra, C.K., Sahoo, S.R. (2022). Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-9873-6_53
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