通过机器学习识别胸部X光图像中的肺炎
Identification of pneumonia on chest x-ray images through machine learning
AI总结：
本文基于迁移学习开发机器学习模型，用于自动识别儿童胸部X光图像中的肺炎，测试敏感性达98%、特异性达97.3%。
摘要：
肺炎是世界上导致婴儿死亡的主要感染性原因。如果能够早期识别，就有可能改变患者的预后，人们可以利用影像学检查来辅助诊断确认。尽快执行和解读检查对于良好治疗至关重要，这种病理最常用的检查是胸部X光。本研究的目标是开发一种软件，用于识别胸部X光片中是否存在肺炎。该软件被开发为基于机器学习的计算模型，使用了迁移学习技术。在训练过程中，图像收集自一个在线可用的数据库，其中包含在中国一家医院拍摄的儿童胸部X光图像。训练完成后，模型被用于处理新图像，在识别该病理方面取得了相关结果，对用于测试的样本达到了98%的敏感性和97.3%的特异性。可以得出结论，开发一种能够识别胸部X光图像中肺炎的软件是可行的。
英文摘要：
Pneumonia is the leading infectious cause of infant death in the world. When identified early, it is possible to alter the prognosis of the patient, one could use imaging exams to help in the diagnostic confirmation. Performing and interpreting the exams as soon as possible is vital for a good treatment, with the most common exam for this pathology being chest X-ray. The objective of this study was to develop a software that identify the presence or absence of pneumonia in chest radiographs. The software was developed as a computational model based on machine learning using transfer learning technique. For the training process, images were collected from a database available online with children's chest X-rays images taken at a hospital in China. After training, the model was then exposed to new images, achieving relevant results on identifying such pathology, reaching 98% sensitivity and 97.3% specificity for the sample used for testing. It can be concluded that it is possible to develop a software that identifies pneumonia in chest X-ray images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
