Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia is a common respiratory infection caused by bacteria, viruses, or fungi and can indiscriminately affect people of all ages. As highlighted by the World Health Organization (WHO), this prevalent disease tragically accounts for a substantial 15% of global mortality in children under five years of age. This article presents a comparative study of the Inception-ResNet deep learning model's performance in diagnosing pneumonia from chest radiographs. The study leverages Mendeleys chest X-ray images dataset, which contains 5856 2D images, including both Viral and Bacterial Pneumonia X-ray images. The Inception-ResNet model is compared with seven other state-of-the-art convolutional neural networks (CNNs), and the experimental results demonstrate the Inception-ResNet model's superiority in extracting essential features and saving computation runtime. Furthermore, we examine the impact of transfer learning with fine-tuning in improving the performance of deep convolutional models. This study provides valuable insights into using deep learning models for pneumonia diagnosis and highlights the potential of the Inception-ResNet model in this field. In classification accuracy, Inception-ResNet-V2 showed superior performance compared to other models, including ResNet152V2, MobileNet-V3 (Large and Small), EfficientNetV2 (Large and Small), InceptionV3, and NASNet-Mobile, with substantial margins. It outperformed them by 2.6%, 6.5%, 7.1%, 13%, 16.1%, 3.9%, and 1.6%, respectively, demonstrating its significant advantage in accurate classification.

翻译：肺部炎症（特别是肺炎）的诊断对有效治疗和管理该疾病至关重要。肺炎是由细菌、病毒或真菌引起的常见呼吸道感染，可无差别地影响所有年龄段人群。世界卫生组织（WHO）指出，这一常见疾病不幸地导致全球五岁以下儿童死亡病例中15%的悲剧性比例。本文对比研究了Inception-ResNet深度学习模型在胸部X光片肺炎诊断中的性能表现。研究利用Mendeley胸部X光图像数据集（包含5856张二维图像，涵盖病毒性和细菌性肺炎X光片），将Inception-ResNet模型与七种其他先进卷积神经网络（CNN）进行对比。实验结果表明，Inception-ResNet模型在提取关键特征和节省计算时间方面具有显著优势。此外，我们探讨了迁移学习结合微调对提升深度卷积模型性能的影响。本研究为使用深度学习模型进行肺炎诊断提供了宝贵见解，凸显了Inception-ResNet模型在该领域的潜力。在分类准确率方面，Inception-ResNet-V2相比其他模型（包括ResNet152V2、MobileNet-V3（大/小型）、EfficientNetV2（大/小型）、InceptionV3和NASNet-Mobile）表现出显著优势，分别以2.6%、6.5%、7.1%、13%、16.1%、3.9%和1.6%的准确率提升超越这些模型，充分体现了其在准确分类方面的突出能力。