使用深度残留网络和遗传算法进行多模式医学图像分类
Muhammad Haris Abid1, Rehan Ashraf1, Toqeer Mahmood1
1Department of Computer Science, National Textile University, Faisalabad, Pakistan.
PloS one
|June 29, 2023
概括
医疗保健中的人工智能 (AI) 显著改善了医疗图像分类. 一个深度学习模型ResNet50实现了98.61%的准确性,提高了诊断能力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 准确的医学图像分类对于诊断和治疗计划至关重要.
- 传统方法在语义差距上扎,需要手动的特征提取.
- 深度学习,特别是卷积神经网络 (CNN),在克服这些局限性方面表现有前途.
研究的目的:
- 为了弥合医学图像分类中的语义差距.
- 通过使用深度学习来提高多模式医疗图像的分类性能.
- 评估ResNet50模型对此任务的有效性.
主要方法:
- 使用了基于深度学习的模型ResNet50.
- 在28378张多模式医学图像的数据集上训练并验证了模型.
- 使用准确度,精度,回忆和F1分数来评估性能.
主要成果:
- 拟议的ResNet50模型实现了98.61%的整体准确性.
- 该模型与其他最先进的方法相比,显示出更高的分类性能.
- 关键的评估参数 (准确性,精度,回忆,F1得分) 证实了该模型的有效性.
结论:
- 深度学习,特别是ResNet50模型,有效地弥合了医学图像分类中的语义差距.
- 开发的模型在准确和一致的诊断决策方面取得了重大进展.
- 这项研究通过改进的诊断工具,直接有利于医疗保健服务.
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