An integrated CAD system facilitating the endometrial cancer diagnosis

In this study we present an integrated system for supporting the diagnosis of endometrial cancer. The system consists of an electronic patient record that incoporates a hysteroscopy imaging CAD system for the early detection of endometrial cancer. The electronic patient record is based on information collected from: appointments, patient info, hysteroscopy reporting and pharmacy. The CAD system is based on ROI manual or semi-automated extraction, texture feature computation and SVM and C4.5 classification into normal/abnormal. The highest percentage of correct classifications score (%CC) for the SVM classifier was 79% for the YCrCb color system using the SF+SGLDS texture feature sets for differentiating between normal vs abnormal ROIs. The C4.5 algorithm gave slightly lower classification scores, but also classification rules. The proposed system offers an integrated platform to the physician for assessing suspicious areas of endometrial cancer. However, further work is needed to validate the system with more cases and more users of the prototype.