DOI: 10.5061/dryad.g1jwstr22
title: Cervical intraepithelial neoplasia acetic acid white images - pre-cancerous lesion three-class classification

description: 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
 by the proposed dual-color space-based image enhancement algorithm. The
 authors design a Hierarchical Multi-Scale Pixel Excitation (HMSPE) module
 for adaptive multi-scale and multi-level local feature extraction. The
 authors apply the structure of the Swin Transformer network with minor
 modifications in the global perception modeling process. In addition, the
 authors propose two feature fusion concepts: adaptive preprocessing and
 superiority-inferiority fusion, and design a feature fusion module based
 on these concepts, which significantly improves the collaborative ability
 of the Transformer branch and the CNN branch.
description: # 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: CIN0.zip **Description:** The acetic acid images of cervical cancer classified as CIN0 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. ## Code/software **HMCFormer_0** is the neural network code that can only accept original images as input, not enhanced images.  **Image_Augmentation_Code** is the code for image augmentation, which can be used to batch process and enhance cervical cancer acetic acid images.  **HMCFormer_E0** is also a neural network code similar to HMCFormer_0, but it requires both original images and enhanced images as input.