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.

通过使用胸片X光图像，开发了一种计算机辅助诊断系统以自动检测肺炎，该系统使用DenseNet-121和ResNet50作为二元分类（肺炎和正常）和多分类（细菌性肺炎，病毒性肺炎和正常）任务的主干，并实现了一种名为Fuzzy Channel Selective Spatial Attention Module（FCSSAM）的通道特定空间注意机制，用于突出显示相关通道的特定空间区域，同时通过主干提取的特征去除无关通道。该方法在公开可用的胸片X光数据集上进行了评估，使用二元和多分类设置，准确率分别为97.15%和79.79%，结果优于最先进的方法。

FA-Net：一种基于模糊注意力的深度神经网络在胸部X光片中的肺炎检测