The outbreak of COVID-19, a contagious respiratory disease, has had a significant impact on people worldwide. To prevent its spread, there is an urgent need for an easily accessible, fast, and cost-effective diagnostic solution. According to studies, COVID-19 is frequently accompanied by coughing. Therefore, the identification and classification of cough sounds can be a promising method for rapidly and efficiently diagnosing the disease. The COVID-19 epidemic has resulted in a worldwide health crisis, and stopping the disease's spread depends on a quick and precise disease diagnosis. COVID-19 has been detected using medical imaging modalities such as chest X-rays and computed tomography (CT) scans due to their non-invasive nature and accessibility. This research provides an in-depth examination of deep learning-based strategies for recognising COVID-19 in medical images. The benefits and drawbacks of various deep learning approaches and their applications in COVID-19 detection are discussed. The study also examines publicly available datasets and benchmarks for evaluating deep learning model performance. Furthermore, the limitations and future research prospects for using deep le
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