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---
license: mit
pipeline_tag: image-classification
tags:
- medical
---
## Model Details
This model is trained on 224X224 Grayscale images which were originally CT-scans
that were transformed into JPG images. The model is a finetuned version of
[Swin Transformer (tiny-sized model)](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224).
I also used this tutorial.[Swin Transformer (tiny-sized model)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb#scrollTo=UX6dwmT7GP91).
## Uses
The model can be used to classify JPG images of CT scans into either cancer positive or
Cancer negative groups.
I think it would work okay for any image classification task.
## Training Data
The model was trained on data originally obtained from the National Cancer Institute
Imaging Data Commons. https://portal.imaging.datacommons.cancer.gov/explore/
The data set used consisted of about 11,000 images which were transformed CT scans
some of which contained Cancerous Nodules and some that did not.
## How to Use
Upload a grayscale JPG into the model inference section and it will cast a prediction.
One comes included in this repo. If the image contains an X, it is a negative cancer image.
If an image name contains a Y it is positive.
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