  CNN-based deep learning models for disease detection have become popular
recently. We compared the binary classification performance of eight prominent
deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet
b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary
classification performance on combined Pulmonary Chest Xrays dataset. Despite
the widespread application in different fields in medical images, there remains
a knowledge gap in determining their relative performance when applied to the
same dataset, a gap this study aimed to address. The dataset combined Shenzhen,
China (CH) and Montgomery, USA (MC) data. We trained our model for binary
classification, calculated different parameters of the mentioned models, and
compared them. The models were trained to keep in mind all following the same
training parameters to maintain a controlled comparison environment. End of the
study, we found a distinct difference in performance among the other models
when applied to the pulmonary chest Xray image dataset, where DenseNet169
performed with 89.38 percent and MobileNet with 92.2 percent precision.
  Keywords: Pulmonary, Deep Learning, Tuberculosis, Disease detection, Xray
