Next Gen Colposcopy: AI Integration for Cervical Cancer Detection

Lalasa Mukku and Vikas Burri

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 4, 473-478

Abstract: The proposed work introduces a next-generation colposcopy solution in which artificial intelligence (AI) can be seamlessly integrated into the colposcope instrument itself for early detection of cervical cancer. Unlike traditional approaches that rely on offline analysis, the developed framework enables real-time processing of colposcopic images directly within the clinical workflow. The system incorporates a series of specialized AI modules: a Swin Transformer for specular reflection removal, a Modified Gaussian Mixture Model optimized with the Mexican Axolotl Algorithm for precise segmentation of the cervix, and an EfficientNet-B0 architecture enhanced with hybrid attention for robust classification of lesions. The dataset includes time-series cervigrams captured under saline, acetic acid, and Lugol’s iodine application, along with corresponding clinical metadata to improve diagnostic reliability. Once integrated into the colposcope’s imaging pipeline, each captured image is automatically processed to generate immediate diagnostic insights, assisting clinicians in on-the-spot lesion detection and classification. This embedded design eliminates the need for manual data transfer or external computation, streamlining examinations and reducing subjectivity in interpretation. The proposed AI-powered colposcope represents a clinically adaptable and scalable tool that can significantly improve early detection rates, particularly in high-volume and resource-limited healthcare settings, ultimately supporting better patient outcomes.

Keywords: Colposcopy; artificial intelligence; deep learning; machine learning; image processing; computer vision (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25111693
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i4:id:1671

DOI: 10.32628/CSEIT25111693

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