A medical image captioning system for TeleOTIVA: Supporting SDGs-oriented cervical precancer screening in Indonesia

Cervical cancer screening using Visual Inspection with Acetic Acid (VIA) remains a critical strategy in resource-limited settings. However, its effectiveness is often hindered by diagnostic variability arising from subjective interpretation. To address this challenge, we introduce TeleOTIVA, an AI-powered system designed to automatically detect and describe cervical lesions from VIA images. The system integrates YOLOv11-based lesion detection and segmentation with a Dense Residual Network and an embedding LSTM-based image captioning module, enabling it to generate clinically meaningful descriptions encompassing lesion borders, surface texture, and anatomical location. The performance of TeleOTIVA demonstrates promising results. Evaluations of the generated captions, compared to expert-annotated ground truth, yielded high scores across multiple metrics: BLEU (0.5711), METEOR (0.6726), and ROUGE-L (0.6929). These results indicate a high degree of n-gram similarity, semantic relevance, grammatical accuracy, and structural alignment with human-generated descriptions. In other words, the model not only mirrors expert-level vocabulary but also captures the clinical essence of VIA image interpretation. This synergy between advanced lesion detection and automated caption generation significantly enhances the accuracy, efficiency, and accessibility of cervical cancer screening. TeleOTIVA thus offers a powerful and scalable diagnostic aid, particularly impactful for improving early detection efforts in underserved and low-resource regions.