A Study on CNN-Based and Handcrafted Extraction Methods with Machine Learning for Automated Classification of Breast Tumors from Ultrasound Images

Abstract

In this paper, we present an efficient procedure for automatically classifying ultrasound images of benign and malignant breast tumors. We evaluated our approach using four openly available datasets and investigated two categories of feature extraction methods: handcrafted methods (Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG)) and methods based on convolutional neural network (CNN) models. For classification, we explored three classifiers: linear support vector machines (SVM), k-nearest neighbors (KNN), and artificial neural networks (ANN). Two experiments were conducted: the first aimed to design a classifier for each individual dataset, whereas the second aimed to develop a unified classifier for the ensemble datasets. The obtained results demonstrate that the ANN classifier associated to the early stopping (ES) criterion, is very effective in both experiments, outperforming KNN and SVM with 100 % accuracy. Additionally, using CNN models as feature extraction methods proved effective. Among these CNNs: ResNet50, InceptionV3 and DenseNet201 achieve 100 % accuracy in the first experiment, while DenseNet201 allows achieving 100 % accuracy in the second experiment. Comparative analysis with existing research demonstrates the competitiveness or superiority of the proposed procedure.