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CNN-based deep learning models for disease detection have become popularrecently. We compared the binary classification performance of eight prominentdeep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNetb0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binaryclassification performance on combined Pulmonary Chest Xrays dataset. Despitethe widespread application in different fields in medical images, there remainsa knowledge gap in determining their relative performance when applied to thesame dataset, a gap this study aimed to address. The dataset combined Shenzhen,China (CH) and Montgomery, USA (MC) data. We trained our model for binaryclassification, calculated different parameters of the mentioned models, andcompared them. The models were trained to keep in mind all following the sametraining parameters to maintain a controlled comparison environment. End of thestudy, we found a distinct difference in performance among the other modelswhen applied to the pulmonary chest Xray image dataset, where DenseNet169performed with 89.38 percent and MobileNet with 92.2 percent precision.  Keywords: Pulmonary, Deep Learning, Tuberculosis, Disease detection, Xray

Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset