Multi-class deep learning architecture for COVID-19, tuberculosis, and pneumonia classification using chest X-ray images.

Abstract

Advancements in medical imaging and deep learning have enabled the development of intelligent systems that assist clinicians in diagnosing complex pulmonary diseases. This study addresses the growing concern over lung abnormalities caused by diseases such as COVID-19, tuberculosis (TB), and pneumonia. We propose a convolutional neural network (CNN)-based multi-class classification framework that uses chest X-ray images to automatically detect COVID-19, TB, pneumonia, and normal conditions. The original publicly available dataset exhibited class imbalance, with significantly fewer COVID-19 cases compared to other categories. To address this, the Synthetic Minority Oversampling Technique (SMOTE) are applied at the feature level, generating a balanced dataset of 6,000 chest X-ray images equally distributed across the four classes. The preprocessing techniques have been used to enhance model generalisation, including image normalization, augmentation, and resizing. We evaluated multiple deep learning architectures, including ResNet-50, EfficientNet, DenseNet, and VGG-19. Among these, VGG-19 achieved the highest test accuracy of 97.5%, with precision, recall, and F1-score all exceeding 96% across classes. This unified deep learning pipeline integrates data preprocessing, feature extraction, and classification. The proposed model is intended as a research framework and is currently non-clinical; however, it demonstrates promising potential and could be further explored for assisting radiologists in diagnostic decision-making.