Implementation of SSL-Vision Transformer (ViT) for Multi-Lung Disease Classification on X-Ray Images - Baasith, Rafi Haqul; Sasongko, Theopilus Bayu; Hadinegoro, Arifiyanto; Saputro, Uyock Anggoro (Journal of Applied Informatics and Computing, 2026)
Chest X-ray imaging is one of the most widely used modalities for lung disease screening; however, manual interpretation remains challenging due to overlapping pathological patterns and the frequent presence of multiple coexisting abnormalities. In recent years, Vision Transformer (ViT) models have demonstrated strong potential for medical image analysis by capturing global contextual relationships. Nevertheless, their performance is highly dependent on large-scale labeled datasets, which are costly and difficult to obtain in clinical settings. To address this limitation, this study proposes a Self-Supervised Learning Vision Transformer (SSL-ViT) framework for multi-label lung disease classification using the CheXpert-v1.0-small dataset. The proposed approach leverages self-supervised pretraining to learn robust and transferable visual representations from unlabeled chest X-ray images prior to supervised fine-tuning. A total of twelve clinically relevant thoracic disease labels are retained, while non-disease labels are excluded to enhance interpretability and reduce confounding effects. Experimental results demonstrate that SSL-ViT achieves a high recall of 0.73 and a peak AUC of 0.75 on the test set, indicating strong sensitivity in detecting pathological cases. Compared to the baseline ViT model, SSL-ViT exhibits a recall-oriented performance profile that is particularly suitable for screening applications, where minimizing false negatives is critical. Furthermore, Grad-CAM visualizations confirm that the model focuses on anatomically meaningful lung regions, supporting its clinical relevance. These findings suggest that SSL-enhanced Vision Transformers provide a robust and effective solution for multi-label chest X-ray screening tasks.