Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15\% and 79.79\% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://github.com/AyushRoy2001/FA-Net.

翻译：肺炎是一种由细菌、真菌或病毒引起的呼吸道感染。它影响许多人，尤其是在污染水平高、生活条件不卫生、过度拥挤且医疗基础设施不足的发展中或不发达国家。肺炎可导致胸腔积液，即液体充满肺部，从而引发呼吸困难。早期诊断对于确保有效治疗和提高生存率至关重要。胸部X光成像是诊断肺炎最常用的方法。然而，胸部X光的视觉检查可能困难且具有主观性。在本研究中，我们开发了一种基于胸部X光图像的自动肺炎检测计算机辅助诊断系统。我们分别使用DenseNet-121和ResNet-50作为二分类（肺炎与正常）和多分类（细菌性肺炎、病毒性肺炎与正常）任务的主干网络。我们还实现了一种称为模糊通道选择性空间注意力模块（FCSSAM）的通道特定空间注意力机制，以突出相关通道的特定空间区域，同时移除主干网络提取特征中的无关通道。我们在公开可用的胸部X光数据集上，使用二分类和多分类设置评估了所提出的方法。我们提出的方法在二分类和多分类设置中分别达到了97.15%和79.79%的准确率。所提方法的结果优于现有最先进（SOTA）方法。所提模型的代码将发布于：https://github.com/AyushRoy2001/FA-Net。