Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines

Abstract

Abstract Background Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed. Methods In this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel. Results Experimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported. Conclusions The proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources.

Keywords

• Kernel methods
• Gaussian process models
• Support vector machines
• Transfer learning
• Cancer prediction