Abstract <p>Worldwide, lung disease is a serious health problem, affecting a large percentage of the global population. Accurate diagnosis of lung diseases can be challenging as many of these conditions present with similar symptoms. The goal of this research is to design a robust deep learning-based technique capable of accurately detecting four types of lung diseases, such as COVID-19, bacterial pneumonia, viral pneumonia, and mycoplasma pneumonia, using computed tomography (CT) images. In this study, a publicly available CT image dataset is used for designing the lung disease detection system. First, CT images are preprocessed to enhance their quality, then a GAN (Generative Adversarial Network)-based augmentation technique is used to expand the dataset, doubling its size. A hybrid deep learning model named LungNet, consisting of a Convolutional Neural Network (CNN) module with a dual attention mechanism, a multi-head attention module, and the proposed RPLSTM (revamped potent Long Short-Term Memory) network module, is designed for lung disease detection. The CNN module extracts useful features from CT images. The multi-head attention module helps to focus on the most significant features extracted by the CNN module. Lastly, the proposed RPLSTM module is used to diagnose lung disease effectively. The designed LungNet model achieves an average detection accuracy of 99.30%. The key innovations of this research are: (1) the design of a novel deep learning architecture called LungNet for different lung disease detection, (2) the utilization of channel attention and spatial attention in the CNN module of the proposed model for robust feature extraction, (3) employing a multi-head attention layer in the designed model to enhance its efficacy, (4) proposing an advanced architecture of potent long short-term memory (PLSTM) called RPLSTM and utilizing it for lung disease detection, and (5) utilization of GAN to increase the dataset size and thus solve the dataset scarcity problem.</p>