The new form of coronavirus started in Wuhan, China, in 2019 and is known as COVID-19. It created severe health issues and also deaths in most of the countries. The test kits and certain imaging techniques, namely computed tomography and X-ray, are utilized to analyze the severity of diseases. Earlier, researchers introduced several machine-learning techniques for medical diagnosis. However, due to complexity concerns and a high error rate, such strategies cannot produce superior results. Recently, several deep learning mechanisms have been utilized in medical diagnosis. In this work, a new triple-task learning architecture is introduced for the identification and categorization of COVID-19 disease by referring to CT images. First, the input images are pre-processed utilizing Gabor filtering and image resizing. After pre-processing, the images are fed to the triple-task learning network. Here, in the proposed network, three modules are included, namely Residual Swin Transformer based U-Net, Deep convolution and Extended BiLSTM. In this, the Residual Swin Transformer-based U-Net performs the segmentation task. After that, the most significant features are extracted using Deep convolution. The extracted features are then used in the classification step when the various classes of COVID-19 are classified. Finally, the classification parameters are fine-tuned utilizing the Adaptive Fire Hawks algorithm. Then, the proposed technique is experimentally verified utilizing a Python tool, and the performance is analyzed by evaluating the performance metrics. Also, the proposed approach is compared to existing techniques, and the comparison results show that the proposed technique achieves better performance, having an accuracy of 99.46%.