Segmentation and classification of mammographic abnormalities using local binary patterns and deep learning

Louai Zaiter; Reyer Zwiggelaar

doi:10.1117/12.3026886

29 May 2024 Segmentation and classification of mammographic abnormalities using local binary patterns and deep learning

Louai Zaiter, Reyer Zwiggelaar

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131741Z (2024) https://doi.org/10.1117/12.3026886
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

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%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Louai Zaiter and Reyer Zwiggelaar "Segmentation and classification of mammographic abnormalities using local binary patterns and deep learning", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131741Z (29 May 2024); https://doi.org/10.1117/12.3026886

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