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.

翻译：肺炎是全球婴儿感染性死亡的首要病因。早期识别可改善患者预后，影像学检查有助于确诊。对该病症最常用的检查手段是胸部X光片，尽快完成检查并解读结果对实现良好治疗至关重要。本研究旨在开发一款能够识别胸部X光片中是否存在肺炎的软件。该软件基于机器学习技术，采用迁移学习方法构建计算模型。训练过程中，我们从公开数据库中收集了某中国医院儿童胸部X光片图像。完成训练后，模型对新图像进行测试，在识别此类病理特征方面取得了显著成效：测试样本的灵敏度达98%，特异度达97.3%。研究结果表明，开发能识别胸部X光图像中肺炎的软件具有可行性。