Cervical Cancer Detection Using Gabor Featured Convolutional Neural Networks

Abstract

Cervical cancer is identified as the most killing disease in women patients from past two decades. Even though various medical treatments are available at present, its death rate is not reduced. Hence, various computational and automated methods are proposed to improve the medical treatments as early as possible at an earlier stage. From the past decades, machine and deep learning methods are used for detecting the cervical cancer images. The deep learning algorithms provide superior results for the cervical cancer detection system. Therefore, Gabor Featured Convolutional Neural Networks (GFCNN) is proposed to detect the cancer regions in cervical images. It proposes Cervical Cancer Detection System (CCDS) is significantly analyzed by applying the methodologies on the cervical images which are available in Guanacaste Dataset. The proposed CCDS is constructed with the following modules Gabor transforms, Feature computations, Classification and Segmentation. The Gabor transform transforms the pixel coordinates and then intrinsic variation features are determined. These features are classified by the GFCNN classifier. The GFCNN method is tested on the set of cervical images in Guanacaste Dataset and its performance is analyzed with respect to precision, recall and cancer accuracy rate. The proposed CCDs method achieved 98.42% of PN, 98.54% of RL and 98.45% of AY. The results of GFCNN method for cervical cancer detection and segmentation system are compared with other state-of-the-art methods.