LAUSR

Cervical cancer accounts for a large number of fatalities among cancer patients. It is ranked fourth in the total cancer patients and total number of deaths due to cancer. Developing countries account for 70% of the cases and 90% of the fatalities. Contemporary techniques used for screening cervical cancer are PAP smear test and HPV DNA test. Today there are treatments that can successfully prevent cervical cancer if detected at an early stage. Understanding the cervix type is very important for treatment; computational methods can help us classify the cervix type from cervical images. In this study, we propose an ROI proposal network EfficientCenterDet and a self‐supervision boosted training trick that improves the performance of the network with relatively less labeled data. We use 6114 unlabeled images to perform a pretraining task and 1166 labeled images to retrain the ROI proposal network. The proposed model matches the state‐of‐the‐art IOU of FasterRCNN on the ISIC skin lesions dataset while using one‐third of the number of parameters used in FasterRCNN. On MobileODT cervical data, our self‐supervision boosted model achieves 0.632 IOU, a 10% boost over the state‐of‐the‐art FasterRCNN. Introducing an ensembled EfficientNet B4, the cervix type classification stage achieved an accuracy of 87%.