Chest radiographs are extensively employed for the detection and diagnosis of several pulmonary conditions, including fibrosis, tuberculosis, and COVID-19. The manual processing of chest X-ray images is a time-consuming and error-prone task, yet its analysis is crucial for timely disease identification and diagnosis. This study utilizes Convolutional Neural Networks and five pretrained models viz. ResNet50, MobileNetV2, InceptionNetV3, Xception, and VGG16 to detect lung infectious diseases using chest X-ray. The main purpose of this work is to assess the performance of deep learning models in diagnosing the lung diseases namely Covid-19, Fibrosis, and Tuberculosis. These models are trained on publicly available datasets and assessed for the performance parameters such as accuracy, recall, precision, F1-score, AUC-score, and false positive, false negative counts. The performance of CNN, and five pretrained models are evaluated and compared with respect to these performance metrics. It has been observed from the results that ResNet50, MobileNetV2, and VGG16 show incredibly high performance for all performance parameters used in this work. To provide early diagnosis and patients outcomes, these automated diagnosis methods can be used by the medical professionals.