Breast Cancer Classification Using Machine Learning Techniques: A Review
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Abstract
Breast cancer remains one of the top diseases that lead to thousands of death in women every year. Artificial intelligence (AI) has been utilized for diagnosis early, rapidly, and accurately breast tumors. The objective of this paper is to review recent studies for classifying these tumors. Machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Random Forest (RF) are used to classify medical images into malignant and benign. Moreover, deep learning has been employed recently for the same purpose, among them, Convolutional Neural Network (CNN) is one of the most popular techniques. The results showed that the SVM achieved high accuracy, about 97%, therefore, the researchers utilized various functions for this algorithm and added more features such as bagging and boosting to increase its efficacy. In addition, deep learning obtained high accuracy using CNN which is higher than 98%.
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