Combination Of Gamma Correction and Vision Transformer In Lung Infection Classification On CT-Scan Images

Lung infection is an inflammatory condition of the lungs with a high mortality rate. Lung infections can be identified using CT-Scan images, where the affected areas are analyzed to determine the infection type. However, manual interpretation of CT-Scan results by medical specialists is often time-consuming, subjective, and requires a high level of accuracy. To address these challenges, this study proposes an automated classification method for lung infections using deep learning techniques. Convolutional Neural Networks (CNNs) are widely used for image classification tasks. However, CNN operates locally with limited receptive fields, making capturing global patterns in complex lung CT images challenging. CNN also struggles to model long-range pixel dependencies, which is crucial for analyzing visually similar regions in lung CT-Scans. This study uses a Vision Transformer (ViT) to overcome CNN limitations. ViT employs self-attention mechanisms to capture global dependencies across the entire image. The main contribution of this study is the implementation of ViT to enhance classification performance in lung CT-Scan images by capturing complex and global image patterns that CNN fails to model. However, ViT requires a large dataset to perform optimally. To overcome these challenges, augmentation techniques such as flipping, rotation, and gamma correction are applied to increase the amount of data without altering the important features. The dataset comprises lung CT-scan images sourced from Kaggle and is divided into Covid and Non-Covid classes. The proposed method demonstrated excellent classification performance, achieving accuracy, sensitivity, specificity, precision, and F1-Score above 90%. Additionally, the Cohen’s kappa coefficient reached 89%. These results show that the proposed method effectively classifies lung infections using CT-Scan images and has strong potential as a clinical decision-support tool, particularly in reducing diagnostic time and improving consistency in medical evaluations.