对超声波图像自动诊断的机器学习技术的调查
Kumar Mohit1, Rajeev Gupta1, Basant Kumar1
1Electronics and Communication Engineering Department, MNNIT Allahabad, Prayagraj, U.P-211004.
Current medical imaging
|May 31, 2023
概括
机器学习算法增强了计算机辅助诊断 (CAD) 用于利用超声波图像进行早期疾病检测. 基于纹理的特征和支持向量机 (SVM) 显示出高准确度,而无色化对于可靠的结果至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 机器学习 (ML) 在计算机辅助诊断 (CAD) 中至关重要,用于自动检测疾病.
- 超声波成像 (USG) 是首选的,因为它的便携性和非侵入性.
- 图像质量通常会因为在采集过程中的噪音和文物而受到损害.
研究的目的:
- 审查超声波图像中CAD的特征提取,选择和分类技术.
- 调查ML应用程序以使用USG诊断四种主要疾病.
- 突出染在提高诊断准确性的作用.
主要方法:
- 审查和表格化各种特征提取,选择和分类方法在以前的研究中使用.
- 基于纹理的灰色层特征和支持向量机 (SVM) 分类器的分析.
- 探索用于提高USG图像质量的无雾化技术.
主要成果:
- 基于纹理的特征和SVM分类器显示出高分类准确性的巨大潜力,在一些研究中达到100%.
- 学生的t测试被认为是选择重要的特征有价值的.
- 在USG图像上改进ML算法的性能方面,除是必不可少的.
结论:
- 机器学习算法,特别是具有纹理特征和SVM分类器的机器学习算法,为精确的超声数据CAD提供了强大的工具.
- 有效的特征选择和强大的无雾化对于最大限度地提高诊断性能至关重要.
- 对无色化技术的进一步研究是必要的,以克服USG的图像质量挑战.
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