CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images

TL;DR

This paper presents a CNN-based method using transfer learning for classifying cervical cancer in whole-slide histopathology images, achieving high accuracy and F1-score, addressing the challenge of limited labeled data.

Contribution

The study introduces a VGG16-based CNN approach with pre-processing for cervical cancer classification in digital slides, demonstrating effective transfer learning in weakly-supervised settings.

Findings

Accuracy of 98.26% achieved

F1-score of 97.9% demonstrated

Effective transfer learning in digital pathology

Abstract

Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. A constantly increasing incidence in Africa making cervical cancer a priority by the World Health Organization (WHO) in terms of screening, diagnosis, and treatment. Conventionally, cancer diagnosis relies primarily on histopathological assessment, a deeply error-prone procedure requiring intelligent computer-aided systems as low-cost patient safety mechanisms but lack of labeled data in digital pathology limits their applicability. In this study, few cervical tissue digital slides from TCGA data portal were pre-processed to overcome whole-slide images obstacles and included in our proposed VGG16-CNN classification approach. Our results achieved an accuracy of 98,26% and an F1-score of 97,9%, which confirm the potential of transfer learning on this weakly-supervised task.