Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images

Abstract

Cervical cancer is a common type of cancer in women worldwide. Detection of this type of cancer in the early stages is very important for the treatment process. Early diagnosis/detection is very important for the treatment of cervical cancer. The golden standard of diagnosing cervical cancer is the pap-smear test. To automatically diagnose cervical cancer, machine learning is a good solution and many computer vision/deep learning-based models have been presented in the literature.In this study, an exemplar pyramid deep feature extraction-based method has been proposed for the detection of cervical cancer. The prime purpose of our proposal is to classify cervical cells in pap-smear images for the detection of cancer. SIPaKMeD and Mendeley Liquid Based Cytology (LBC) datasets have been used to develop our exemplar pyramid deep feature generator. The phases/steps of the proposed exemplar pyramid structure-based model are; (i) transfer learning-based feature extraction using DarkNet19 or DarkNet53 networks in an exemplar pyramid structure and the proposed feature generator creates 21,000 features. By deploying Neighborhood Component Analysis (NCA), the most informative/weighted 1000 features from the generated 21,000 features. The selected 1000 features by NCA are classified with the Support Vector Machine (SVM) algorithm. Both 5-fold cross-validation and hold-out validation (80:20) have been utilized as validation techniques. The best accuracies for the SIPaKMeD and Mendeley LBC datasets have been computed as 98.26% and 99.47%, respectively. The obtained results illustrate that the proposed exemplar pyramid model is successful to diagnose cervical cancer using pap-smear images.