肺CT图像自动分割中统计方法与深度学习方法的比较研究

Dr. Akey Sungheetha, Dr. Rajesh Sharma R
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引用次数: 17

摘要

近年来,深度学习技术在医学成像精准任务的图像分割领域发挥着重要的作用。在诊断的一个关键组成部分,深度学习是一个有组织的网络,具有均匀的区域,以提供准确的结果。用统计模型自动分割方法在许多关键条件下证明了它的优越性。在本文中,我们重点研究了与保守的自动分割方法相比,系统过程的准确性和速度的提高。并比较了深度学习方法中不同算法的准确率、灵敏度、特异性、精密度、均方根误差、查准率-查全率曲线等性能指标。本文的对比研究内容是构建一种高效、准确的肺CT图像分割模型。
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Comparative Study: Statistical Approach and Deep Learning Method for Automatic Segmentation Methods for Lung CT Image Segmentation
Recently, deep learning technique is playing important starring role for image segmentation field in medical imaging of accurate tasks. In a critical component of diagnosis, deep learning is an organized network with homogeneous areas to provide accurate results. It is proved its superior quality with statistical model automatic segmentation methods in many critical condition environments. In this research article, we focus the improved accuracy and speed of the system process compared with conservative automatic segmentation methods. Also we compared performance metrics such as accuracy, sensitivity, specificity, precision, RMSE, Precision- Recall Curve with different algorithm in deep learning method. This comparative study covers the constructing an efficient and accurate model for Lung CT image segmentation.
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