AI-assisted diagnosis of cervical dysplasia from cervicography images

Cervicography using Visual Inspection with Acetic Acid (VIA) is widely adopted for cervical cancer screening in low-resource settings. Although effective for identifying visible lesions, VIA cannot determine the severity of dysplasia, limiting its diagnostic utility. This study proposes a multi-task learning framework combined with an ensemble mechanism to estimate lesion severity directly from cervicography images. Four clinically relevant features—color, surface texture, lesion position, and lesion area across quadrants—learned through deep learning model and aggregated using ensemble method resulting a severity decision. Five datasets were used across training, testing, and prediction stages; Swede Score annotations supported model training, while histopathology-confirmed images from IARC were used for validation to ensure reliable ground truth. To address data scarcity, StyleGAN-2 with adaptive discriminator augmentation (ADA) was employed to generate synthetic images for augmentation. Initial multi-task learning experiments achieved 62% accuracy. After applying GAN-based augmentation and ensemble learning, performance improved substantially, reaching 95.21% accuracy, 95.08% sensitivity, and 81.25% precision for mild cases, and above 95% across all metrics for severe cases. Despite these promising results, the study has several limitations, including dataset imbalance, reliance on synthetic GAN-generated images, variability in imaging modalities across datasets, and a relatively small test set. These factors may affect the model’s generalizability in broader clinical applications. Overall, the findings highlight the potential of the proposed approach to enhance the diagnostic value of VIA-based cervicography, while underscoring the need for larger datasets and more extensive clinical validation.