Texture‐based feature extraction of smear images for the detection of cervical cancer

Abstract

In India, cervical cancer is the second most common type of cancer in females. Pap smear is a simple cytology test for the detection of cancer in its early stages. To obtain the best results from the Pap smear, expert pathologist are required. Availability of pathologist in India is far below the required numbers, especially in rural parts. In this paper, multiple texture‐based features are introduced for the extraction of relevant and informative features from single‐cell images. First‐order histogram, GLCM, LBP, Laws, and DWT are used for texture feature extraction. These methods help to recognise the contour of the nucleus and cytoplasm. ANN and SVM are used to classify the single‐cell images either normal or cancerous based on the trained features. ANN and SVM are used on every single feature as well as on the combination of all features. Best results are obtained with a combination of all features. The system is evaluated on generated dataset MNITJ, containing 330 single cervical cell images and also on publicly available benchmark Herlev data set. Experimental results show that the proposed texture‐based features give significantly better results in cervical cancer detection when compared with state of the art shape‐based features regarding accuracy.