Browsing by Author "Kahesa, Crispin"

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    A systematic review on computer vision-based methods for cervical cancer detection
    (Elsevier, 2026) Mbelwa, Hope; Leo, Judith; Kahesa, Crispin; Mkoba, Elizabeth
    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.