Segmentation and Classification of Mammographic Abnormalities using Local Binary Patterns and Deep Learning

This study aims to develop machine learning and deep learning algorithms to segment and classify mammography images into benign and malignant types of tumors. The author will show that handcrafted features can give results similar to deep-learned features. To perform this comparison, we evaluate the performance of two kinds of algorithms. In both cases, we use multi-Otsu threshold methods to segment mammography images. The first algorithm uses a local binary pattern feature extractor, a principal component analysis algorithm to reduce the dimensions, and traditional machine learning classifiers such as the multilayered perceptron, the random forest, and the support vector machine. The second algorithm uses pre-trained convolutional neural networks, such as the AlexNet and the VGG19, along with a softmax classifier. We evaluated our algorithms on the MIAS and INbreast datasets and we found that the model that uses the local binary pattern along with a support vector machine recorded an accuracy of 56.7% and the deep learning model that uses the AlexNet along with a softmax classifier recorded an accuracy of 73%.