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使用基于特征工程开发的CBIR系统检测胆囊疾病类型

Ahmet Bozdag¹, Muhammed Yildirim², Mucahit Karaduman³

¹Department of General Surgery, School of Medicine, Firat University, Elazığ 23119, Turkey.

Diagnostics (Basel, Switzerland)

|March 13, 2025

概括

人工智能 (AI) 有助于利用基于内容的图像检索 (CBIR) 早期检测胆囊疾病. 这种人工智能模型准确地识别了胆囊疾病,提高了诊断效率.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
诊断工具诊断工具诊断工具

背景情况:

早期发现胆囊 (GB) 疾病对于改善患者的治疗结果至关重要.
延迟或不准确的GB疾病诊断可能会导致较差的临床结果和症状增加.
对GB疾病的超声波图像进行解释需要专门的专业知识,手动分析是耗时和资源密集的,特别是在偏远地区.

研究的目的:

开发和评估一个基于人工智能的基于内容的图像检索 (CBIR) 系统,用于早期检测胆囊疾病.
与传统方法相比,提高诊断GB疾病的效率和准确性.

主要方法:

利用人工智能 (AI) 技术,包括机器学习 (ML) 和深度学习 (DL),重点是基于内容的图像检索 (CBIR).
开发的CBIR模型整合了三个不同的预训练架构的特征,用于特征提取.
在图像比较中采用了共弦值法作为相似度测量指标.

主要成果:

与其他六种卷积神经网络 (CNN) 模型和两种纹理模型相比,拟议的CBIR模型表现出优异的性能.
实现了0.94的平均精度 (AP) 值,表明疾病检测的高精度.
该模型成功地结合了多个预训练架构的特征,以有效地提取特征.

结论:

开发的CBIR模型显示了早期检测和诊断胆囊疾病的巨大潜力.
高AP值证实了该模型在识别GB疾病方面的有效性和可靠性.
这种人工智能驱动的方法为克服与手动超声波图像解释相关的挑战提供了有希望的解决方案.

关键词:

印度央行 (CBIR) 的.人工智能的人工智能是人工智能.这种癌症是癌症癌症.胆囊炎是一种胆囊炎.胆结石疾病胆结石疾病

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