A statistical evaluation of the preprocessing medical images impact on a deep learning network’s performance

R. Ivanescu
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引用次数: 1

Abstract

The aim of this paper is to explore the efficiency of preprocessing medical images before applying a deep learning algorithm to classify the data. The study uses a statistical framework that establishes the fact that depending on the dataset used, image preprocessing indeed decreases the computational time, without having a dropdown in performance. The dataset used in this study regard colon cancer, lung cancer, and fetal brain ultrasound scans. The study proposes a statistical performance that studies the performances of the ResNet50 deep learning network in different preprocessing scenarios.
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医学图像预处理对深度学习网络性能影响的统计评估
本文的目的是探讨在应用深度学习算法对数据进行分类之前对医学图像进行预处理的效率。该研究使用了一个统计框架,根据所使用的数据集,图像预处理确实减少了计算时间,而不会降低性能。本研究使用的数据集涉及结肠癌、肺癌和胎儿脑超声扫描。本研究提出了一个统计性能,研究了ResNet50深度学习网络在不同预处理场景下的性能。
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CiteScore
1.10
自引率
10.00%
发文量
18
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