A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method

Abstract

In contemporary times, machine learning is being used in almost every field due to its better performance. Here, we consider different machine learning methods such as logistic regression, random forest, support vector classifier (SVC), AdaBoost classifier, bagging classifier, voting classifier, and Xception model to classify the breast cancer tumor and evaluate their performances. We used a standard dataset, i.e., breast Histopathology images, that has more than two lakhs color patches, each patch of size $$50\times 50$$ scanned at the resolution of 40 $$\times $$ . We use 60% of the above-mentioned dataset for training, 20% for validation, and 20% testing to all above-mentioned classifiers. The logistic regression classifier provides the scores of each precision, recall, and F1 measure as 0.72. The random forest method provides the score of each precision, recall, and F1 score as 0.80. The bagging and voting classifiers both have the values of each precision, recalls, and F1 scores as 0.81. In this case, both SVC and AdaBoost classifiers have the score of each precision, recall, and F1 score as 0.82, whereas in the case of the deep learning method, Xception model is used to have the score of each precision, recall, and F1 measure as 0.90 in the same condition. Thus, the Xception method performs the best among all mentioned methods in terms of each of the performance measures, i.e., precision, recall, and F1 score for the classification of breast cancer tumors. Thus, the importance of this research work is that we can classify tumors more accurately in less time. It may increase awareness of people toward breast cancer and decrease fears of tumors.