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.

该研究旨在开发一种能够在胸部X光片中识别肺炎是否存在的软件，该软件采用基于机器学习的迁移学习技术建立了一个计算模型。经过训练后，该模型在新图像中表现出显著的肺炎识别结果，对于测试样本，敏感性达到98％，特异性达到97.3％。因此可以得出结论，可以开发出一种能够在胸部X光片中识别肺炎的软件。

通过机器学习在胸部X射线照片中识别肺炎