Abstract
Cervical cancer is the fourth most common type of cancer and is also a leading cause of mortality among women across the world. Various types of screening tests are used for its diagnosis, but the most popular one is the Papanicolaou smear test, in which cell cytology is carried out. It is a reliable tool for early identification of cervical cancer, but there is always a chance of misdiagnosis because of possible errors in human observations. In this paper, an auto-assisted cervical cancer screening system is proposed that uses a convolutional neural network trained on Cervical Cells database. The training of the network is accomplished through transfer learning, whereby initializing weights are obtained from the training on ImageNet dataset. After fine-tuning the network on the Cervical Cells database, the feature vector is extracted from the last fully connected layer of convolutional neural network. For final classification/screening of the cell samples, three different classifiers are proposed including Softmax regression (SR), Support vector machine (SVM), and GentleBoost ensemble of decision trees (GEDT). The performance of the proposed screening system is evaluated for two different testing protocols, namely, 2-class problem and 7-class problem, on the Herlev database. Classification accuracies of SR, SVM, and GEDT for the 2-class problem are found to be 98.8%, 99.5%, and 99.6%, respectively, while for the 7-class problem, they are 97.21%, 98.12%, and 98.85%, respectively. These results show that the proposed system provides better performance than its previous counterparts under various testing conditions.
Highlights
Cervical cancer is the leading cause of cancer-related deaths in females
We have proposed two scenarios with different classifiers, i.e., Support vector machine (SVM) and GentleBoost ensemble of decision trees (GEDT). e mean values of accuracy, F1 score, area under the curve (AUC), sensitivity, and specificity of fine-tuned ConvNet 2 with GEDT classifier are 99.6%, 99.14%, 99.9%, 99.30%, and 99.35%, respectively, for the 2-class problem. ese are 98.85%, 98.77%, 99.8%, 98.8%, and 99.74%, respectively, for the 7class problem
Unlike previous methods, which are based upon cytoplasm/nucleus segmentation and hand-crafted features, our method automatically extracts deep features embedded in the cell image patch for classification. is system requires cells with coarsely centered nucleus as the network input
Summary
The Herlev dataset [34] only contains 917 cervical cells with 675 abnormal and 242 normal cells that are insufficient for ConvNets To overcome this limitation, recently, image data augmentation techniques have been proposed to virtually increase the size of training datasets and reduce the problem of overfitting [25]. E publicly available Herlev Pap smear dataset is used for the training and testing purpose It contains 917 single cervical cell images with ground truth classification and segmentation. E proposed approach, like previous patch-based classification methods, does not directly operate on original images present in the Herlev dataset that contains multiple cells at a time [40,41,42,43]. The count of correct classification score is obtained for each cell from all the categories in the Herlev dataset
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