Cervix Type and Cervical Cancer Classification Using Deep Learning Technique

Abstract

Cervical cancer is the most common cancer among women worldwide. It is screened using visual inspection after application of acetic acid (VIA), Papanicolaou (Pap), and Human papillomavirus (HPV) test and diagnosed by histopathology test. Currently, identification of cervix type and histopathologic image classification is examined manually, thus big inter-and intra- observer variability exists. Moreover, incidence and mortality rates are consistently increasing in developing countries due to a shortage of screening facilities, skilled professionals, and lack of awareness. Therefore, there is a need for an automated system that overcomes subjectivity and inconsistency in the screening process. Different studies have proposed the classification of cervix type and cervical cancer using deep learning and machine learning techniques. However, most of the proposed techniques in the literature are limited to specific class classification. This study aims to develop an automatic cervix type and cervical cancer classification using deep learning techniques. For cervix type classification 3872 colposcopy images were collected from the Kaggle dataset. In addition, 133 colposcopy images and 915 histopathologic images were collected from local health facilities for cervix and cervical cancer classification from local health facilities. Different pre-trained models including VGGNet, ResNet, Ensemble of MobileNet, and ResNet and EfficientNet were trained and compared for cervix type classification. Prior to classification, a lightweight MobileNetv2-YOLOv3 model was created for the region of interest (ROI) extraction. The extracted cervix images were then fed to the effecientnet_b0 model for cervix type classification. A mean average precision (MAP) of 99.88% for the region of interest (ROI) extraction, an accuracy of 97%, and a Kappa score of 0.95 for the cervix type classification task were achieved using the selected models. For cervical cancer classification, an efficient_b0 pre trained model was trained and validated using histogram matched histopathological images, and an accuracy of 95% and Kappa score of 0.92 were achieved. Even though the best performing model was constructed further improvement needs to be done using more data and by classifying sub-class of histopathology images to know disease prognosis. In addition, to make full cervical cancer screening and diagnosis method cytology image classification model construction is required