自主引导的残留深度神经网络与多尺度扩展特征提取用于超声波成像中的自动胆囊疾病诊断
Suzain Rashid1, Chandan J Das2, Anshika Chauhan1
1Amity School of Engineering and Technology, Amity University, Noida, 201313, India.
Computer methods and programs in biomedicine
|August 21, 2025
概括
一个新的深度学习模型准确诊断9种胆囊疾病, 准确率达到了99.17%. 这种人工智能工具提供了一种可靠且有效的方法,
科学领域:
- 医学成像
- 人工智能
- 深度学习
背景情况:
- 如果不及时诊断, 胆囊疾病会带来严重的健康风险.
- 疾病并发症包括胆管阻塞,慢性疼痛,感染,败血症和胆囊癌.
研究的目的:
- 开发一种新的深度学习模型来诊断九种不同的胆囊疾病.
- 提高临床实践中的诊断准确性和效率.
主要方法:
- 使用注意力引导的残余卷积神经网络.
- 嵌入多尺度的特征提取与扩展的卷曲.
- 使用注意力机制和剩余连接来精确的特征选择和信息保存.
主要成果:
- 获得了99.17%的准确性和98.94%的回忆.
- 在区分各种胆囊病理方面表现出高可靠性.
- 该模型作为一个快速,准确和可扩展的诊断工具.
结论:
- 人工智能模型显示了在胆囊疾病诊断中提高患者护理的潜力.
- 为强大,高效和可扩展的人工智能辅助临床诊断提供基础.
- 源代码是公开的.
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