基于概括的分数优化可解释的轻量级CNN模型用于疟疾疾病分类
Zeshan Aslam Khan1, Muhammad Waqar1, Muhammad Junaid Ali Asif Raja2
1International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan.
Computers in biology and medicine
|December 22, 2024
概括
这项研究引入了一种用于疟疾诊断的新,可解释的深度学习模型. 轻量级的卷积神经网络实现了高精度,为公共卫生提供了更快,更有效的解决方案.
科学领域:
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
背景情况:
- 深度学习 (DL) 在医疗图像处理方面表现出色,与传统方法相比,它具有优势.
- 疟疾是由Plasmodium falciparum引起的,构成了全球重大公共卫生威胁.
- 现有的疟疾诊断DL模型显示出希望,但在计算效率和可解释性方面面临挑战.
研究的目的:
- 为疟疾诊断开发一种基于分数顺序的可解释的轻质卷积神经网络 (CNN).
- 解决当前DL方法中的计算低效率和缺乏可解释性的局限性.
- 提供一个具有成本效益和时间效率的疟疾诊断工具.
主要方法:
- 提出了一种新的轻量级CNN架构,结合了分数顺序优化.
- 通过使用标准NIH数据集,MP-IDB数据集和M5测试集来训练和验证模型.
- 使用准确性,精度,回忆和F1得分指标评估模型性能.
主要成果:
- 在NIH数据集上实现了95%的准确性,在速度和有效性方面超过了复杂的现有模型.
- 证明了强大的概括性,在MP-IDB数据集上准确率为92%,在M5测试集上准确率为90.4%.
- 该模型的有效性进一步得到了强大的精度,回忆和F1分数值的证实.
结论:
- 提出的基于分数顺序的可解释轻量级CNN为疟疾诊断提供了改进和高效的解决方案.
- 该模型的高精度,速度和通用性突出显示了其在现实世界公共卫生应用中的潜力.
- 这项研究有助于推进人工智能驱动的传染病诊断工具.
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