一种基于深度学习的胸部X射线图像预测新生儿呼吸系统疾病的新方法

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Ayse Erdogan Yildirim , Murat Canayaz
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引用次数: 0

摘要

近年来,在医学领域,利用深度学习模型可以在短时间内诊断出许多疾病。这一领域的大多数研究都集中在成人或儿科患者身上。然而,深度学习在新生儿疾病诊断方面的研究还不够充分。此外,众所周知,肺炎等呼吸系统疾病在新生儿死亡原因中占很大比例,因此对新生儿呼吸系统疾病的早期准确诊断至关重要。因此,我们的研究旨在利用新生儿重症监护病房住院患者的胸部x线图像,通过开发的深度学习方法来检测呼吸系统疾病的存在。因此,C+EffxNet的增强版本,新的混合深度学习模型,旨在预测新生儿呼吸系统疾病。在该版本中,将PCA选择的特征组合为100、200和300,然后进行二值分类处理。在本研究中,特征合并前的精度和kappa值分别为0.965和0.904,特征合并后的精度和kappa值分别为0.977和0.935。该方法是为诊断新生儿呼吸系统疾病而开发的,随后也被应用于文献中经常用于诊断儿科肺炎的胸部x线数据集。对于该数据集,准确率为0.992,kappa值为0.982。得到的结果证实了该方法在两个数据集上的成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images

In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.

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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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