基于机器学习的瘤诊断,使用MRI图像中的压缩传感
Nimmy Ann Mathew1, Ishita Maria Stanley1, Renu Jose1
1Department of Electronics and Communication, Rajiv Gandhi Institute of Technology, Kottayam, Kerala, 686501. Affiliated to APJ Abdul Kalam Technological University, Kerala, India.
Biomedical physics & engineering express
|July 31, 2023
概括
压缩传感 (CS) 加快磁共振成像 (MRI) 分析,以更快,更准确的疾病诊断. 将CS与VGGNet-16等深度学习 (DL) 模型相结合,在对健康和瘤MRI图像进行分类时实现了98.7%的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 磁共振成像 (MRI) 产生了大量的数据集,这给及时诊断疾病带来了挑战.
- 压缩传感 (CS) 提供了一种从更少的数据点重建图像的方法,可能加快MRI分析.
- 深度学习 (DL) 模型在医学图像分析中表现有前途,但往往需要大量的数据.
研究的目的:
- 研究将压缩传感 (CS) 与深度学习 (DL) 结合起来,以提高MRI分析的有效性.
- 开发和评估一种模型,使用CS重建的数据将MRI图像分类为健康或不健康.
- 评估CS增强型MRI在提高瘤诊断准确性和效率方面的潜力.
主要方法:
- 采用了混合方法,将压缩传感 (CS) 与VGGNet-16深度学习 (DL) 模型相结合.
- 使用CS原则重建了MRI图像,包括正常和瘤数据集.
- VGGNet-16模型在CS重建的图像上进行了训练,用于二进制分类 (健康与不健康).
- 使用准确性,精确性,回忆和F1分数等指标来评估性能.
主要成果:
- 与CS增强的VGGNet-16模型实现了MRI图像的高分类准确率98.7%.
- 获得的准确性与使用传统获得的MRI数据的方法相提并论.
- 该研究证明了使用CS用于高效的MRI数据采集和分析的可行性.
结论:
- 压缩传感 (CS) 与深度学习 (DL) 结合,显著提高了基于MRI的瘤诊断的效率和准确性.
- 这种方法具有临床应用的潜力,能够更快,更可靠地检测疾病.
- 进一步研究CS在各种医学成像模式中的应用是有必要的,以扩大诊断能力.
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