Cervical intraepithelial neoplasia acetic acid white images - pre-cancerous lesion three-class classification

Cervical cancer ranks first in incidence among malignant tumors of the female reproductive system, and 80% of women who die from cervical cancer worldwide are from developing countries. VIA screening based on artificial intelligence-assisted diagnosis can provide a cheap and rapid screening method. This will attract more low-income women to volunteer for regular cervical cancer screening. However, current AI-based VIA screening studies either have low accuracy or require expensive equipment assistance. In this paper, we propose the HMCFormer (Hierarchical Multi-Scale Convolutional Transformer) network, which combines the hierarchical feature extraction capability of CNNs and the global dependency modeling capability of Transformers to address the challenges of realizing intelligent VIA screening. HMCFormer can be divided into a Transformer branch and a CNN branch. The Transformer branch receives unenhanced lesion sample images, and the CNN branch receives lesion sample images enhanced b..., , , # Cervical intraepithelial neoplasia acetic acid white images - pre-cancerous lesion three-class classification

[https://doi.org/10.5061/dryad.g1jwstr22](https://doi.org/10.5061/dryad.g1jwstr22)

## Description of the data and file structure

The compressed files named “CIN0, CIN1, CIN2_3” contain cervical cancer acetic acid image data.

### Files and variables

#### File: CIN1.zip

**Description:** The acetic acid images of cervical cancer classified as CIN1 are composed of two images stitched together. One image is enhanced using the image enhancement algorithm, while the other is the original image. The image enhancement algorithm is the one used in this project.

#### File: CIN2_3.zip

**Description:** The acetic acid images of cervical cancer classified as CIN2_3 are composed of two images stitched together. One image is enhanced using the image enhancement algorithm, while the other is the original image. The image enhancement algorithm is the one used in this project.

#### File...,