Enhancing detection of cervical cancer through deep learning: a comparative study of histological image-based algorithms

Tjale, Palesa

Enhancing detection of cervical cancer through deep learning: a comparative study of histological image-based algorithms

Thesis / Dissertation

2025

Publisher

University of Cape Town

Department

Department of Integrative Biomedical Sciences (IBMS)

Faculty

Faculty of Health Sciences

Abstract

Cervical cancer is a significant contributor to cancer-related deaths among women worldwide, especially in low- and middle-income countries (LMICs) where access to screening services is limited. Early detection plays a vital role in improving patient outcomes. However, traditional diagnostic techniques, including Pap smears and histological assessments, are often affected by variability, subjectivity, and limited sensitivity. Advances in artificial intelligence (AI), particularly deep learning (DL) and visual prompting methods, offer new possibilities for enhancing the accuracy, efficiency, and interpretability of cervical cancer detection from histology images. In this thesis, I investigate the application of DL models—ResNet50, SqueezeNet, EfficientNet, and a Visual Prompting Model—for classifying cervical cells using histopathological images. I conduct a comparative analysis to evaluate these models based on accuracy, sensitivity, specificity, and interpretability. To enhance model explainability, I employ Grad-CAM to visualize model decisions, offering insights into the diagnostic relevance of highlighted features. My results indicate that the Visual Prompting Model outperforms conventional DL models, achieving the highest accuracy (98%) and F1-score (0.99) while also demonstrating superior localization of diagnostically significant regions. EfficientNet follows closely with an accuracy of 97% and an F1-score of 0.97, while SqueezeNet achieves 95% accuracy and an F1-score of 0.95. In contrast, ResNet50 shows lower performance, with an accuracy of 91% and an F1-score of 0.91, indicating limitations in feature extraction and localization. A key finding of my study is that integrating visual prompting significantly enhances model explainability, addressing a critical challenge in AI-driven medical imaging. By directing attention to clinically relevant areas within histological images, visual prompting reduces misclassification rates, potentially aiding pathologists in making more informed diagnostic decisions. Additionally, the computational efficiency and ease of training of Visual Prompting Models suggest their feasibility for deployment in resource-constrained settings where expert pathology review is limited. Overall, my findings underscore the transformative potential of AI, particularly visual prompting, in improving cancer detection. These AI-assisted diagnostic tools promise not only to enhance accuracy but also to improve interpretability, making them highly relevant for clinical integration. I suggest that future research should focus on validating these AI models across diverse clinical settings, optimizing computational efficiency, and exploring hybrid AI approaches that incorporate molecular and genomic data for a more comprehensive approach to cervical cancer diagnostics.