  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.
