In the fight against COVID-19, accurate and timely patient diagnosis is crucial to control the disease and prevent its spread effectively. A recent study examined transfer learning from various architectures, such as Densenet, Gernet, and SeNet, and employed decoder architectures like UNet++, Deeplabv3, and Deeplabv3+ to reproduce pulmonary and COVID- 19 infection regions from the features and achieve the optimal results. Remarkable results were obtained using two public datasets that included both positive and negative slices. Specifically, Densenet161 integrated with UNet++ achieved the highest scores in specificity, sensitivity, Dice coefficient, and Intersection over Union (IoU), with values of 87.6%, 91.7%, 89.6%, and 81.1%, respectively, in the considered architectures. Refining these algorithms can equip medical professionals with the most effective tools for quick and accurate COVID- 19 diagnosis. Doctors can save time, reduce costs, and effectively combat this pandemic with such tools.