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基于乳腺 X 光图像的机器学习方法辅助乳腺癌检测与诊断的研究综述。

Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.

机构信息

Division of Computer Science and Engineering, School of Engineering, CUSAT, Cochin, India.

出版信息

Curr Med Imaging. 2023;19(12):1361-1371. doi: 10.2174/1573405619666230213093639.

Abstract

Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intelligent, accurate, and automatic computeraided detection (CAD) systems with improved learning capability. In recent years, deep learning-based ML approaches developed to improve the diagnostic capabilities of CAD systems. This study reviews image enhancement, ML and DL methods for breast cancer detection and diagnosis using mammogram images and provides an overview of these methods. The analysis of different ways of ML and DL shows that the usages of traditional ML approaches are limited. However, DL techniques have an excellent future for implementing medical image analysis and improving the ability to exist CAD systems. Despite the significant advancements in deep learning methods for analyzing medical images to detect breast cancer, challenges still exist regarding data quality, computational cost, and prediction accuracy.

摘要

机器学习(ML)在医学图像处理研究领域中起着至关重要的作用。ML 技术的优势使得更智能、更准确、更自动的计算机辅助检测(CAD)系统具有更好的学习能力。近年来,基于深度学习的 ML 方法的发展提高了 CAD 系统的诊断能力。本研究综述了使用 mammogram 图像进行乳腺癌检测和诊断的图像增强、ML 和 DL 方法,并对这些方法进行了概述。对不同 ML 和 DL 方法的分析表明,传统 ML 方法的应用受到限制。然而,DL 技术在实施医学图像分析和提高 CAD 系统的能力方面具有很好的前景。尽管在利用深度学习方法分析医学图像以检测乳腺癌方面取得了重大进展,但在数据质量、计算成本和预测准确性方面仍存在挑战。

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