深度学习在胸部 X 光异常检测中的应用

Nhan Ngo, Toi Vo, Lua Ngo
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引用次数: 0

摘要

肺部疾病是世界上最常见的疾病,在越南尤其如此。某些胸腔肺部疾病甚至会给患者带来危险。X 射线是检测胸部异常的有效成像方式。此外,人工智能可以提高对 X 光图像异常的检测,减少误诊,缩小医生之间的知识差距,减轻医生的压力。因此,本研究旨在应用深度学习技术检测胸部 X 光图像中的异常情况,并利用数据科学和统计方法提高深度学习模型的性能。我们对数据进行了探索和处理,以获得具有最佳特征的高质量数据。然后,我们在特征金字塔网络(FPN)骨干中使用 ResNet101 对 RetinaNet 模型进行了数据增强和优化,以达到最佳性能。我们的模型在验证集中的平均精度达到了 0.55,阈值为 0.5 (mAP@0.5),验证集包括五种疾病:主动脉扩大、心脏肿大、间质性肺病、浸润和结节/肿块。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of deep learning in chest X-ray abnormality detection
Lung diseases are the most common diseases worldwide, especially in Vietnam. Certain thoracic lung diseases can even lead to dangerous conditions for patients. X-ray are a useful imaging modality for detecting the abnormalities in the chest area. Furthermore, artificial intelligence can improve the detection of abnormalities in X-ray images, reduce misdiagnosis, close the knowledge gap between doctors, and alleviate the pressure on doctors. Therefore, this study aims to apply deep learning techniques to detect abnormalities in chest X-ray images and use data science and statistical approaches to improve the performance of the deep learning model. The data was explored and processed to obtain high quality data with optimal characteristics. We then applied data augmentation and optimization to the RetinaNet model with ResNet101 in a Feature Pyramid Network (FPN) backbone to achieve the best performance. Our model achieved mean average precision of 0.55 at a threshold of 0.5 (mAP@0.5) in a validation set, which included five diseases: aortic enlargement, cardiomegaly, interstitial lung disease, infiltration, and nodule/mass.
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