使用深度学习检测胆囊疾病类型:一个信息化的医学方法
Ahmed Mahdi Obaid1, Amina Turki2, Hatem Bellaaj3
1CEMLab, National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3029, Tunisia.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了一个深度神经网络模型,用于从超声波图像中诊断九种胆囊疾病. 移动网络模型实现了98.35%的准确性,使得有效的早期疾病检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 在医疗保健中,早期和准确的疾病诊断至关重要.
- 胆囊疾病带来了诊断挑战,需要先进的工具.
- 深度学习为分析超声波等医疗图像提供了潜力.
研究的目的:
- 开发和评估深度学习模型,以使用超声波图像同时对9种胆囊疾病进行分类.
- 为培训和验证创建一套全面的胆囊超声图像数据集.
主要方法:
- 来自三家医院的1782名患者的10,692张胆囊超声图像的平衡数据集.
- 图像经过预处理和增强以进行细分.
- 应用了四种深度神经网络模型,并对疾病分类进行了比较.
主要成果:
- 所有四种深度神经网络模型都显示出有效的胆囊疾病检测.
- 移动网络模型在分类九种不同的胆囊疾病时,达到98.35%的最高准确率.
结论:
- 深度学习模型,特别是MobileNet,在通过超声波图像诊断多种胆囊疾病方面表现出高效.
- 这种方法促进了早期和准确的检测,潜在地改善了患者的治疗结果.
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