Background and objectives: Pneumonia is a disease that affects the lungs, making breathing difficult. Nowadays, pneumonia is the disease that kills the most children under the age of five in the world, and if no action is taken, pneumonia is estimated to kill 11 million children by the year 2030. Knowing that rapid and accurate diagnosis of pneumonia is a significant factor in reducing mortality, acceleration, or automation of the diagnostic process is highly desirable. The use of computational methods can decrease specialists’ workload and even offer a second opinion, increasing the number of accurate diagnostics.

Methods: This work proposes a method for constructing a specific convolutional neural network architecture to detect pneumonia and classify viral and bacterial types using Bayesian optimization from pre-trained networks.

Results: The results obtained are promising, in the order of 0.964 accuracy for pneumonia detection and 0.957 accuracy for pneumonia type classification.

Conclusion: This research demonstrated the efficiency of CNN architecture estimation for detecting and diagnosing pneumonia using Bayesian optimization. The proposed network proved to have promising results, despite not using common preprocessing techniques such as histogram equalization and lung segmentation. This fact shows that the proposed method provides efficient and high-performance neural networks since image preprocessing is unnecessary.

背景和目的：肺炎是一种影响肺部的疾病，使呼吸困难。如今，肺炎是世界上导致五岁以下儿童死亡人数最多的疾病，如果不采取行动，预计到 2030 年，肺炎将导致 1100 万儿童死亡。降低死亡率、加速或诊断过程自动化的重要因素是非常可取的。计算方法的使用可以减少专家的工作量，甚至可以提供第二意见，增加准确诊断的数量。

方法：这项工作提出了一种构建特定卷积神经网络架构的方法，以使用来自预训练网络的贝叶斯优化来检测肺炎并分类病毒和细菌类型。

结果：获得的结果是有希望的，肺炎检测准确度为 0.964，肺炎类型分类准确度为 0.957。

结论：这项研究证明了 CNN 架构估计在使用贝叶斯优化检测和诊断肺炎方面的效率。尽管没有使用常见的预处理技术，如直方图均衡和肺分割，但所提出的网络被证明具有良好的结果。这一事实表明，由于不需要图像预处理，所提出的方法提供了高效且高性能的神经网络。