Structure recognition on mammography images using neural network and feature selection

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.

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Publication Details

Published
2024-08-16
DOI
https://doi.org/10.1117/12.3028271
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

Structure recognition on mammography images using neural network and feature selection

Сергей Воронин, Dmitrii Zhernov, Алексей Воронин, Vitaly Kober et al.
AI in cancer detection
article

Structure recognition on mammography images using neural network and feature selection

Сергей Воронин, Dmitrii Zhernov, Алексей Воронин, Vitaly Kober, Artyom S. Makovetskii
article en

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.

Chelyabinsk State University (RU), Centro de Investigación Científica y de Educación Superior de Ensenada (MX)
Openalex Percentile: Top 89%
AI in cancer detection
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