An efficient cervical disease diagnosis approach using segmented images and cytology reporting

Cervical cancer is the second most common cancer in women globally. A computer aided cervical disease diagnosis system that can relieve pressure on medical experts and save the cost is proposed. To implement our approach in the reality of cervical diseases diagnosis, a multi-modal framework is designed for three kinds of cervical diseases diagnosis that integrates uterine cervix images, Thinprep Cytology Test, human papillomavirus test, and patients’ age. However, too many features increase memory storage costs and computational costs, and it affects the spread of this system in poor areas. Feature selection not only eliminates redundant or irrelevant features but also finds the factors that influence the disease most first is performed in multi-modal frameworks for cervical diseases diagnosis. The detailed process of the method is as follows: first, according the representative color, an efficient image segmentation algorithm is developed; then from three different types of segmented images, we extract color features and texture features for interpreting uterine cervix images; next, Boruta algorithm is applied to feature selection; finally, the performance of Random Forests that utilizes selected features for cervical disease diagnosis is investigated. In the experiment, the proposed multi-modal diagnostic approach gives the final diagnosis for three different kinds of cervical diseases with 83.1% accuracy, which significantly outperforms methods using any single source of information alone. The validation cohort is applied to validate the efficiency of our method, and the performance of random forest obtained by using only 1.2% of features is like or even better than using 100% of features.