Liang Hailun, Hu Meili, Ma Yuxin, Yang Lei, Chen Jie, Lou Liwei, Chen Chen, Xiao Yuan
School of Public Administration and Policy, Renmin University of China, Beijing 100872, China.
Department of Gynecology, Baoding Maternal and Child Health Care Hospital, Baoding 071000, China.
Life (Basel). 2023 Sep 14;13(9):1911. doi: 10.3390/life13091911.
For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy.
We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search. We conducted searches for appropriate articles in the PubMed and ISI Web of Science databases and chose those that employed deep learning to classify or predict lung nodule malignancy for our investigation. The figures were plotted, and the data were extracted using SAS version 9.4 and Microsoft Excel 2010, respectively.
Sixteen studies that met the criteria were included in this study. The articles classified or predicted pulmonary nodule malignancy using classification and summarization, using convolutional neural network (CNN), autoencoder (AE), and deep belief network (DBN). The AUC of deep-learning models is typically greater than 90% in articles. It demonstrated that deep learning performed well in the diagnosis and forecasting of lung nodules.
It is a thorough analysis of the most recent advancements in lung nodule deep-learning technologies. The advancement of image processing techniques, traditional machine learning techniques, deep-learning techniques, and other techniques have all been applied to the technology for pulmonary nodule diagnosis. Although the deep-learning model has demonstrated distinct advantages in the detection of pulmonary nodules, it also carries significant drawbacks that warrant additional research.
多年来，计算机技术一直被用于诊断肺结节。与传统的用于图像处理的机器学习方法相比，深度学习方法可以通过避免图片繁琐的预处理步骤（提取虚假特征等）来提高肺结节诊断的准确性。我们的目标是研究深度学习方法对肺结节恶性肿瘤的分类效果如何。
我们通过系统的文献检索评估了深度学习方法在肺结节恶性肿瘤分类方面的性能。我们在PubMed和ISI Web of Science数据库中搜索了合适的文章，并选择那些采用深度学习来分类或预测肺结节恶性肿瘤的文章进行我们的研究。分别使用SAS 9.4版和Microsoft Excel 2010绘制图表并提取数据。
本研究纳入了16项符合标准的研究。这些文章使用分类和汇总、卷积神经网络（CNN）、自动编码器（AE）和深度信念网络（DBN）对肺结节恶性肿瘤进行分类或预测。文章中深度学习模型的AUC通常大于90%。这表明深度学习在肺结节的诊断和预测方面表现良好。
这是对肺结节深度学习技术最新进展的全面分析。图像处理技术、传统机器学习技术、深度学习技术等技术的进步都已应用于肺结节诊断技术。尽管深度学习模型在肺结节检测中已显示出明显优势，但它也存在重大缺陷，值得进一步研究。
