Computer Aided Diagnosis of Cervical Cancer Using HOG Features and Multi Classifiers

Abstract

Cervical cancer is very common in women, and it is the most dreaded disease. Cervical cancer if detected early can be treated successfully. Cervical cancer occurs due to the uncontrolled growth of the cells present in the cervix of the female body, and it also occurs due to the virus human papilloma virus (HPV). Pathologists diagnose cervical cancer by a screening test called Papanicolaou test or Pap smear test. The pap smear test is not always 100% accurate but it helps in early detection of cancerous cells. In this paper, a method is proposed that helps in detection and classification of the cancer using HOG feature extraction and classifying it by the help of support vector machine (SVM), k-nearest neighboring (KNN), artificial neural network (ANN). The database was collected from Air Force Command Hospital, Bengaluru. A total of 66 pap smear images were collected that are 25 normal pap smear images and 41 abnormal pap smear images. Histogram of gradient (HOG) extracts features of the region of interest in the image as it converts pixel-based representation into gradient-based representation. The classification of cervical cells—abnormal cells and normal cells—is done with the help of multi-classifier. The accuracy attained after classification is 62.12, 65.15, and 95.5% for SVM, KNN, and ANN, respectively.