ABSTRACT COVID‐19 is a rapidly spreading infectious disease that has posed significant challenges to global healthcare systems. Chest computed tomography (CT) plays a crucial role in detecting pulmonary abnormalities associated with COVID‐19, especially when RT‐PCR tests yield false‐negative results. However, the limited availability of annotated CT datasets, class imbalance, and inter‐patient variability significantly affect the reliability of deep learning‐based diagnostic systems. To address these challenges, this paper proposes an augmented artificial intelligence framework that integrates deep convolutional generative adversarial networks (DCGAN), U‐Net‐based lung segmentation and deep convolutional neural networks (DCNN) for accurate COVID‐19 diagnosis using CT images. A DCGAN model is employed to synthesise additional CT images, where synthetic samples are generated class‐wise and added only to the training set after patient‐wise data separation, thereby expanding the dataset from 672 real CT images (345 COVID‐19 and 327 other pneumonia cases) to 2672 images while reducing class imbalance. The synthesised and real CT images are processed through a U‐Net architecture to segment lung regions and enhance the focus on pathological features. Subsequently, classification is performed using multiple DCNN architectures, including AlexNet, VGG‐16, VGG‐19, ResNet50 and DenseNet. A structured four‐phase experimental evaluation is conducted to independently analyse the impact of augmentation and segmentation, ensuring transparent performance comparison. Strict patient‐wise data splitting is enforced prior to augmentation to prevent data leakage and ensure unbiased generalisation. Experimental results demonstrate that the combined use of synthesised data and lung segmentation significantly improves diagnostic performance, with AlexNet achieving the highest accuracy of 97.6%, F1‐score of 0.97 and specificity of 0.98. The proposed framework provides a reliable and computationally efficient solution to support radiologists in rapid COVID‐19 screening and clinical decision‐making.
SureshKumar et al. (2026) studied this question.