Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

Abstract

This research presents a novel approach for cervical cancer detection and segmentation using tissue images with multiple cells. The study employs a novel deep learning architecture based on Mask Region-Based Convolutional Neural Network (RCNN) and statistical analysis. This new architecture enables us to achieve a high percentage of detection and pix-to-pix area segmentation. A mean Average Precision (mAP) higher than 60% for 3-class and 5-class were achieved. In addition, higher F1-scores of 70% for 3-class and 5-class were obtained. This investigation is a collaborative work, where a medical consultant collected the samples from the Papanicolaou (Pap) Smear examination and labeled the cells presented to the liquid-based cytology (LBC). Furthermore, the online available benchmark data set, SIPaKMeD , was also utilized. Additionally, sample images from the Mendeley data set were also labeled by the trained medical consultant for comparison. The proposed scheme automatically generates a full report for a medical consultant to identify the location of the malicious cells in the given images and expedite the diagnosis and treatment process.