A systematic review on computer vision-based methods for cervical cancer detection
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Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Cervical cancer is a leading cause of mortality among women globally, especially in regions where access to
timely screening remains a challenge. With such concerns, accurate detection of cervical lesions is essential for
effective diagnosis and treatment. This review aimed to explore the application of computer vision-based
methods for detecting cervical cancer, identifying their potential, setbacks and areas for future development.
A comprehensive literature search across Scopus, IEEE Xplore, PubMed, and Google Scholar identified 96 relevant studies published between 2014 and August 2025. These studies applied computer vision methods including
CNNs, Vision Transformers, and multimodal models to cervical cancer detection using Pap smear, colposcopy,
and histopathology images. They were analyzed based on the techniques employed, datasets used, evaluation
metrics adopted, and reported results. This review highlights significant advancements in the field, particularly
in lesion classification, precise segmentation of affected regions, and accurate detection of cancerous regions.
However, some challenges were identified, including limited image datasets with insufficiently distributed
normal and abnormal cases, aggravated by privacy issues and accurate labeling of medical images, which is
critical and rigorous, often leading to annotation inconsistencies. Lastly, this study revealed that integrating
Natural Language Processing and Computer Vision can enhance cervical cancer diagnosis through multi-modal
models that combine both clinical text and imaging data. Additionally, this study proposes the use of techniques
like annotation-efficient learning to manage limited labeled datasets using methods such as semi-supervised and
transfer learning as well as the use of federated learning to ensure privacy in computer-aided diagnostic systems.
Sustainable Development Goals
SDG-3: Good Health and Well-Being
SDG-5: Gender Equality
Keywords
Cervical cancer, Classification, Computer vision, Medical imaging, Object detection, Pap smear, Segmentation