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MixNet-LD:使用修改后的MixNet模型对多种肺部疾病的自动分类系统

Ayesha Ahoor¹, Fahim Arif¹, Muhammad Zaheer Sajid¹

¹Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan.

Diagnostics (Basel, Switzerland)

|October 28, 2023

概括

这项研究引入了MixNet-LD,这是一种用于分类肺部疾病严重程度的自动化系统. 这种新的方法达到98.5%的准确性,改善了对肺炎和肺癌等疾病的医学图像分析.

科学领域:

医学成像和人工智能医学成像和人工智能
计算机辅助诊断计算机辅助诊断
呼吸系统医学呼吸系统医学

背景情况:

肺部疾病对健康构成重大风险,需要准确的严重程度评估才能有效管理.
目前的治疗重点是控制不可逆转的肺部疾病严重程度.
需要自动化,一致的方法来可靠地确定肺部疾病的强度.

研究的目的:

开发一种自动化的方法,MixNet-LD,用于识别和分类肺部疾病的严重程度.
为了利用一个升级的预先训练的MixNet模型来增强肺部疾病的分类.
提高医疗图像分析在诊断肺部疾病的准确性和效率.

主要方法:

开发了MixNet-LD,这是一个使用预先训练的MixNet模型的自动化系统.
使用Grad-Cam实现了预处理策略,用于降低噪音和突出显示功能.
利用数据增强来进行数据集平衡,并使用密集块来改进分类.
使用SVM分类器将图像分类为正常,COVID-19,肺炎,结核病和肺癌等类别.

主要成果:

MixNet-LD实现了具有可管理的模型复杂性的最先进性能.
该系统的准确性很高,在一个具有挑战性的肺病数据集上达到98.5%.
在各种数据集上进行测试,包括新的Pak-Lungs数据集.

结论:

MixNet-LD有效地提高了医学图像分析中的分类准确性.
拟议的方法为肺部疾病检测提供了更好的性能和学习能力.
这项研究有助于为临床应用开发先进的医疗图像处理策略.

关键词:

在 COVID-19 疫情中,深度学习 (DL) 是指深度学习.深度神经网络是一个神经网络.它们是密集的块块.功能损失的功能损失的功能.肺炎是一种肺炎.放射学放射学是指放射学结核病是一种肺结核病.

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