ArticleData in brief2026
BiCervi: Pap smear microscopy image dataset for cervical cancer detection and classification.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- BiCervi: Pap smear microscopy image dataset for cervical cancer detection and classification.Data in brief · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer is still a major public-health challenge, especially in low and middle-income countries where there are limited numbers of specialists to review Pap smears. While Pap smear screening is widely used, manual reading is slow and can vary from one expert to another, which makes data-driven decision support increasingly important. However, Pap smear imagery datasets from African screening settings are still limited, slowing down the development and fair evaluation of machine-learning models. This article presents a curated dataset of Pap smear microscopy images collected at The Ocean Road Cancer Institute (ORCI) in Tanzania. Images were obtained using a digital microscope with a 10 × /0.25 objective lens, and stored as RGB JPEG images at a resolution of a 2592 × 1944 pixels. The Bethesda System for reporting cervical cytology was used to label the images and organize them into eight diagnostic categories: Atypical Squamous Cells of Undetermined Significance (ASC-US), Atypical Squamous Cells, cannot exclude High-grade Squamous Intraepithelial Lesion (ASC-H), Atypical Glandular Cells (AGC), High-grade Squamous Intraepithelial Lesion (HSIL), Low-grade Squamous Intraepithelial Lesion (LSIL), Negative for Intraepithelial Lesion or Malignancy (NILM), Squamous Cell Carcinoma, and Adenocarcinoma. Labels were independently reviewed by two experts for consistency, and disagreements were resolved through adjudication to reach a final decision; images that could not be labeled with sufficient confidence were excluded during quality control. The resulting dataset comprises 3,000 images and can be used to develop Artificial Intelligence-based tools for cervical cancer detection.
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