Many people around the world are affected by pulmonary disease as well as asthma, pneumonia, lung cancer, and tuberculosis. All of these conditions have one thing in common: airway obstruction. Lung illnesses are a worldwide issue in which chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and fibrosis are few of the most common. An accurate diagnosis of a lung problem is essential, and it has been pursued by many researchers using image processing and machine learning models. Multiple deep learning methods are used to predict lung disease; these include convolutional neural networks, vanilla neural networks, visual geometry group-based neural networks, and the capsule network. Medical data are scarce, so diagnosing pulmonary diseases using chest x-ray pictures from datasets with less than 1000 samples is considered to address the problem. Three deep learning multiple neural networks (DLMNNs) were generated using the chicken swarm optimization (CSO) approach, for which transfer learning was applied to evaluate the performance of each. First, we created an algorithm for segmenting CXR images, and then we compared it with other classification systems. Our results were compared with those of other methods using publicly available data from the Shenzhen and Montgomery lung datasets. However, our technique has a lower number of trainable parameters compared with the best-performing models trained on the Montgomery dataset. The DLMNN-CSO virtually matched the best performance on the Shenzhen dataset, although it was computationally less expensive than the other models. DLMNN-CSO’s validation loss was 0.4, whereas the validation loss for CNN, VDSNet, VGG, and DBN was 0.9, 0.8, 0.6, and 0.5, respectively.