利用胸部 X 光片检测肺部感染的级联深度学习模型

Akash Chaturvedi, Shivank Soni
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引用次数: 0

摘要

这项工作的重点是利用胸部 X 光成像这一该领域的可靠方法,准确预测肺部疾病(如奥米克隆和肺炎)。该研究采用迁移学习模型从胸部 X 光图像预测肺部感染。提出的架构包括训练和测试功能,关键步骤包括预处理、深度特征提取和分类。首先,通过数字滤波增强每张 X 光图像,以提高质量。然后,将这些经过处理的图像输入一个稳健的分步学习模型,从而有效地促进特征的自动学习。这种方法的亮点是级联学习模型,不仅准确率高达 99%,而且大大降低了计算复杂度。这体现在训练参数的数量更少,使模型更高效、更轻便,因此在临床应用中区分渺小和肺炎更实用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-Rays
This work focuses on efforts for accurately predicting lung diseases like omicron and pneumonia using chest X-ray imaging, a reliable method in this domain. The work adopts a transfer learning model for lung infection predictions from chest X-ray images. The proposed architecture encompasses both training and testing functions, with key steps including pre-processing, deep feature extraction, and classification. Initially, each X-ray image is enhanced through digital filtering for quality improvement. These processed images are then input into a robust, step-wise learning model that efficiently facilitates the automatic learning of features. The highlight of this approach is the Cascaded learning model, which not only achieves a high accuracy rate of 99% but also significantly reduces computational complexity. This is evidenced by a lower number of training parameters, making the model both more efficient and lightweight, and hence more practical for clinical applications in differentiating between omicron and pneumonia.
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