Detecting and localizing cervical lesions in colposcopic images with deep semantic feature mining

This study aims to investigate the feasibility of employing artificial intelligence models for the detection and localization of cervical lesions by leveraging deep semantic features extracted from colposcopic images.

The study employed a segmentation-based deep learning architecture, utilizing a deep decoding network to integrate prior features and establish a semantic segmentation model capable of distinguishing normal and pathological changes. A two-stage decision model is proposed for deep semantic feature mining, which combines image segmentation and classification to categorize pathological changes present in the dataset. Furthermore, transfer learning was employed to create a feature extractor tailored to colposcopic imagery. Multi-scale data were bolstered by an attention mechanism to facilitate precise segmentation of lesion areas. The segmentation results were then coherently mapped back onto the original images, ensuring an integrated visualization of the findings.

Experimental findings demonstrated that compared to algorithms solely based on image segmentation or classification, the proposed approach exhibited superior accuracy in distinguishing between normal and lesioned colposcopic images. Furthermore, it successfully implemented a fully automated pixel-based cervical lesion segmentation model, accurately delineating regions of suspicious lesions. The model achieved high sensitivity (96.38%), specificity (95.84%), precision (97.56%), and f1 score (96.96%), respectively. Notably, it accurately estimated lesion areas, providing valuable guidance to assisting physicians in lesion classification and localization judgment.

The proposed approach demonstrates promising capabilities in identifying normal and cervical lesions, particularly excelling in lesion area segmentation. Its accuracy in guiding biopsy site selection and subsequent localization treatment is satisfactory, offering valuable support to healthcare professionals in disease assessment and management.