Since the increasing risk of COVID-19, a set of actions have been achieved to develop tools to handle the spreading of the COVID-19 disease. Though testing kits were being used to diagnose the COVID19 infection, the process requires time and the test kits suffer from being lack. In COVID-19 management, the computed tomography (CT) is considered an important diagnostic method. Taking into account large number of exams performed in high case-load situations, an automated method may help to encourage and save time for diagnosing and identifying the disease. Several deep learning tools have recently been developed for COVID-19 scanning in CT scans as a technique for COVID-19 automation and diagnostic assistance. This article aims to explore the rapid recognition of COVID-19 and proposes an advanced deep learning technique, derived from improving the ResNet architecture as a transfer learning model. The architecture design of the proposed model is based on alleviating the connections between the blocks of the ResNet-50 model. This reduces the training time for scale-ability and handles the problem of vanishing gradient with relevant features for recognizing COVID-19 from CT images. The proposed model is evaluated using two well-known datasets of COVID-19 CT examined with a patient-based split. The proposed model attains a total back- bone accuracy of 98.1% with 97%, and 98.6% specificity and sensitivity, respectively.