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一个多类深度学习模型,用于早期肺癌和慢性病的检测,使用计算机断层扫描图像

Ananya Bhattacharjee¹, Sameh Rabea², Abhishek Bhattacharjee³

¹Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India.

Frontiers in oncology

|June 19, 2023

概括

这项研究引入了改进的Xception深度神经网络,用于早期肺癌和慢性病检测. 人工智能模型实现了高精度,帮助放射科医生和科医生进行诊断.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.
腎臟病學 (nephrology) 是一種醫學專業.

背景情况:

肺癌和慢性病 (CKD) 是全球严重的健康问题.
早期检测至关重要,因为它往往无症状呈现,并可能导致严重并发症.
人工智能 (AI) 在提高诊断准确性和速度方面表现有前途.

研究的目的:

开发和评估一个修改的Xception深度神经网络,用于从计算机断层扫描 (CT) 图像中对肺部和脏疾病进行多类分类.
评估模型在早期识别肺癌和各种脏疾病方面的表现.

主要方法:

采用了修改后的Xception深度神经网络架构,利用转移学习与ImageNet权重.
该模型为肺部和脏CT扫描的自动多类图像分类进行了微调.
使用包括准确性,精度,回忆和F1分数在内的指标来评估性能.

主要成果:

该模型实现了99.39%的准确性,99.33%的精度,98%的回忆率和98.67%的F1分数用于肺癌分类.
对于病分类,该模型获得了100%的准确性,精度,回忆和F1分数.
与原始Xception模型和现有方法相比,修改后的Xception模型表现出更高的性能.

结论:

拟议的AI模型对肺癌和慢性病的早期检测非常有效.
这种计算机辅助诊断工具可以在临床实践中显著支持放射科医生和科医生.
高精度和性能指标表明,在医学诊断中具有宝贵的应用.

关键词:

人工智能的人工智能是人工智能.计算机断层扫描 (CT) 是一种计算机断层扫描.精细调整精细调整精细调整脏疾病脏疾病肺癌是一种肺癌.修改了Xception模型的模型.转移学习转移学习

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