Structure recognition on mammography images using neural network and feature selection

Sergei Voronin; Artyom Makovetskii; Vitaly Kober; Dmitrii Zhernov; Aleksei Voronin

doi:10.1117/12.3028271

30 September 2024 Structure recognition on mammography images using neural network and feature selection

Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Dmitrii Zhernov, Aleksei Voronin

Author Affiliations +

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131371G (2024) https://doi.org/10.1117/12.3028271
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Conference Poster

Abstract

Mammography screening also leads to a high rate of false positive results. This may lead to unnecessary worry, inconvenient follow-up care, additional imaging studies, and sometimes the need for tissue. blood draws (often a needle biopsy). Convolutional neural networks (CNN) are one of the most important networks in the field of deep learning. The neural networks form some feature vectors often contain weak features. There are known methods for eliminating weak features based on the mutual information. In this paper, we propose a convolutional neural network based to recognize local geometrical features. Computer simulation results are provided to illustrate the performance of the proposed method.

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

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Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Dmitrii Zhernov, and Aleksei Voronin "Structure recognition on mammography images using neural network and feature selection", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131371G (30 September 2024); https://doi.org/10.1117/12.3028271

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