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深度学习算法在使用CT图像检测和分类肝病方面的性能评估.

R V Manjunath¹, Anshul Ghanshala², Karibasappa Kwadiki³

¹Department of Electronics &Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore-82, India.

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|June 26, 2023

概括

一个新的深度学习模型从计算机断层扫描 (CT) 图像准确地检测和分类肝脏瘤. 这种先进的技术显著提高了肝脏疾病的诊断性能,超过了现有的方法.

关键词:

癌瘤的癌症是什么胆性瘤是一种胆性瘤.计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.有标签的图像转移转移转移转移乌内特网络乌内特网络

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科学领域:

医疗成像医学成像
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

计算机断层扫描 (CT) 成像对于诊断肝脏疾病至关重要.
由于肝脏的复杂性,CT图像的精确瘤特征 (类型,大小,严重程度) 对放射科医生来说具有挑战性.
开发计算机辅助诊断工具对于改善肝病诊断至关重要.

研究的目的:

引入一种新的深度学习模型,用于CT图像中检测和分类肝脏瘤.
为了区分转移和胆管癌肝脏瘤.
评估模型的性能与现有算法对比.

主要方法:

开发一种新的深度学习模型.
该模型应用于计算机断层扫描图像,用于肝脏瘤检测和分类.
使用准确性,子相似系数 (DSC) 和特异性对模型性能进行比较分析.

主要成果:

拟议的深度学习模型在检测和分类肝脏瘤方面表现出卓越的性能.
达到了98.59%的高子相似系数,表明了出色的细分精度.
该模型在各种数据集中显示出强的性能,并且超过了已建立的算法.

结论:

这种新型的深度学习模型提供了使用CT图像进行肝脏瘤诊断的高度准确和可靠的方法.
这种计算机辅助的方法有可能显著提高肝脏疾病的诊断能力.
该模型在分类瘤类型 (转移与胆管癌) 的有效性有助于治疗规划.