Abstract:
Cervical cancer is the 4th most common cancer among women, worldwide. Incidence and mortality rates are consistently
increasing, especially in developing countries, due to the shortage of screening facilities, limited skilled professionals, and lack of
awareness. Cervical cancer is screened using visual inspection after application of acetic acid (VIA), papanicolaou (Pap) test, human
papillomavirus (HPV) test and histopathology test. Inter- and intra-observer variability may occur during the manual diagnosis
procedure, resulting in misdiagnosis. The purpose of this study was to develop an integrated and robust system for automatic cervix
type and cervical cancer classification using deep learning techniques.
Methods: 4005 colposcopy images and 915 histopathology images were collected from different local health facilities and online
public datasets. Different pre-trained models were trained and compared for cervix type classification. Prior to classification, the region
of interest (ROI) was extracted from cervix images by training and validating a lightweight MobileNetv2-YOLOv3 model to detect the
transformation region. The extracted cervix images were then fed to the EffecientNetb0 model for cervix type classification. For
cervical cancer classification, an EffecientNetB0 pre-trained model was trained and validated using histogram matched histopatholo gical images.
Results: Mean average precision (mAP) of 99.88% for the region of interest (ROI) extraction, and test accuracies of 96.84% and
94.5% were achieved for the cervix type and cervical cancer classification, respectively.
Conclusion: The experimental results demonstrate that the proposed system can be used as a decision support tool in the diagnosis of
cervical cancer, especially in low resources settings, where the expertise and the means are limited.
