Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train–test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models’ effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage.

Human cervical cancer and pre-cancer research relies on datasets scattered across modality-specific archives, imaging repositories, benchmark platforms, trial registries, and controlled-access catalogs. This fragmentation—combined with heterogeneous metadata, ambiguous use of “cervical” terminology, and inconsistent indexing of pre-cancer and screening/triage resources—limits reproducible discovery, access planning, and cross-modal benchmarking. We present the Cervical Cancer Dataset Catalog (CCDCAT), a machine-readable, versioned dataset of datasets that enumerates host-specific dataset-instance records anchored to stable identifiers and resolvable landing records within an explicitly declared discoverable source universe (U_v1.0) and a frozen discovery/labeling lexicon (Q_v1.0). The CCDCAT spans invasive cervical cancer, pre-cancer/dysplasia, and cervix-focused screening and triage phenotypes, and it covers molecular omics, imaging and microscopy (including cervix photography, cytology, and digital pathology), trial registry records, benchmark resources, and controlled-access catalogs represented as metadata with explicit access pathways. Eligibility and labels are assigned conservatively from source-provided metadata; when evidence is insufficient, the CCDCAT abstains rather than infers. In the initial release (CCDCAT-U_v1.0; v0.1), we enumerate 14 eligible dataset instances across 11 host systems within a declared universe of 21 sources. Releases include manuscript-ready tables and interoperable artifacts (schema, controlled vocabularies, provenance logs, abstention ledgers, and a queryable database), enabling reproducible filtering, linkage, and auditable reuse planning.