Deep learning and the Transformer algorithm for chest radiograph processing are integrated in TransCov, a breakthrough COVID-19 detection method. TransCov treats images as sequences, which is different from traditional approaches and allows for better understanding of spatial dependencies. After undergoing training on several datasets, the model has remarkable sensitivity and specificity, and its attention processes contribute to its interpretability. TransCov is a viable option for quick, accurate, and understandable COVID-19 diagnosis since it is resilient to picture fluctuations and performs better than standard models. It provides a transformer algorithm with cough audio inputs for TransCov, a deep learning-based COVID-19 detection system. When it comes to pandemic management, it is important to prioritize timeliness for prompt intervention, aim for 91% accuracy to assure dependable diagnosis, and employ a non-invasive technique to improve accessibility and user comfort during illness screening. The datasets used include the complexity of the algorithms used, the quantity and quality of training data, and the particular task being addressed (e.g., severity assessment, binary classification of COVID-19 vs. non-COVID-19 cases, etc.). But with the right preprocessing methods and algorithm design, studies and applications have claimed accuracy levels between 80% and 95%, demonstrating how useful this dataset is for COVID-19 research and diagnosis.Trained on several datasets, the transformer-based model demonstrates resilience to picture changes. TransCov is an innovative answer in the search for cutting-edge COVID-19 diagnostic instruments. With a Transformer-based model that yields an amazing 91% accuracy in COVID-19 identification, this system makes use of the power of deep learning in medical imaging.