Convolutional Neural Networks (CNNs) show great promise as a quick and accurate way to identify COVID-19-positive individuals. CNNs are a subclass of deep learning algorithms that work especially well on image classification tasks. In order to detect COVID-19, scientists have used CNNs to examine pictures from CT scans or chest X-rays in an effort to find distinctive patterns connected to the virus. A popular approach uses a sizable dataset of chest CT or X-ray pictures that have been classified as positive or negative for COVID-19 to train the CNN. Through this process, the network learns to recognize visual cues that are characteristic of COVID-19, which makes it possible for it to categorize new images as either positive or negative for the virus. The use of CNNs for COVID-19 detection has several benefits, chief among them being their ability to analyze large amounts of data quickly and accurately. CNNs are therefore especially well-suited for use in medical contexts where accurate and timely diagnosis is critical. Furthermore, CNNs are essential for detecting COVID-19 in medical pictures due to their high levels of accuracy in image categorization. In addition, continuous improvements in CNN architectures and techniques keep these neural networks’ overall COVID-19 detection efficacy higher. Scholars are presently investigating novel methodologies to enhance the sensitivity and specificity of CNNs, guaranteeing their resilient performance in a range of clinical contexts. This exciting field has the potential to make more advancements and support continuing efforts to mitigate the COVID-19 pandemic's worldwide effects.