Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.

翻译：肺炎是由细菌或病毒引起的呼吸道感染，影响大量人群，尤其在污染严重、生活条件不洁、人口密度高且医疗基础设施不足的发展中和贫困国家尤为常见。胸腔积液（一种因肺部充满液体而导致呼吸困难的病症）由肺炎引发。早期检测肺炎对于确保有效治疗和提高生存率至关重要。诊断肺炎最常用的方法是胸部X光成像。本研究旨在开发一种从数字X光片中自动诊断细菌性和病毒性肺炎的方法。本文首先介绍了作者的技术，随后全面报告了可靠肺炎诊断领域的最新进展。在本研究中，我们调整了最先进的深度卷积神经网络以基于图像分类植物病害并测试其性能。通过实证比较了多种深度学习架构，包括VGG19、ResNet-152v2、Resnext101、Seresnet152、Mobilenettv2和DenseNet-201。实验数据包含两组X光图像：患病组和健康组。为尽快针对植物病害采取相应措施，快速病害识别模型更受青睐。实验表明，DenseNet-201未出现过度拟合或性能下降，且其准确率随训练轮次增加而提升。此外，DenseNet-201在参数数量显著减少、计算时间合理的情况下实现了最先进的性能。该架构在测试准确率上以95%的评分超越其他竞争者。所有架构均使用Keras进行训练，并以Theano作为后端。