Comparative analysis of classical machine learning and deep learning techniques for predicting benign-malignant breast cancer
Keywords:
machine learning; deep learning; benign; malignant; breast cancerAbstract
Classical machine learning methods and deep learning techniques have been widely used for early detection of breast cancer. We do not know which method is best used to classify benign-malignant breast cancer. Therefore, the purpose of this research is to compare classical machine learning and deep learning techniques. The method we use to compare classical machine learning and deep learning techniques is first we take secondary data from the results of digital mammography X-ray photos, then we cropping with a size of 2 cm. then we count 90 features on mammography, 90 features are used as input variables from machine learning. For the CNN method we use 2 types of image sizes, namely full image and 2 cm cropping image, then we calculate the True Positive (TP), False Positive (FP), False Negative (FN), True Negative (TN), accuracy, sensitivity, specificity, precision, TPR and FPR values. Then the value is compared with existing machine learning algorithms such as Random Forest, Naïve Bayes, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), Support Vector Machine (SVM). The results obtained by Deep learning techniques with CNN cropping 2 cm algorithm has the best performance, accuracy, sensitivity, specificity, highest precision and lowest false positive and false negative compared to other methods such as Random Forest, Naïve Bayes, Decision Tree, KNN, ANN, SVM, CNN full image. Conclusion Deep learning techniques with CNN cropping 2 cm algorithm is best used to predict benign-malignant breast cancer.
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